rl_methods.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-27T05:01:24.721042881Z",
     "start_time": "2023-05-27T05:01:19.514415979Z"
    }
   },
   "outputs": [],
   "source": [
    "import riskfolio as rf\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pickle\n",
    "import random\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import gym\n",
    "from gym import spaces\n",
    "\n",
    "from stable_baselines3 import PPO, A2C, TD3, SAC, DDPG, DQN\n",
    "from stable_baselines3.common.env_util import make_vec_env\n",
    "from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize, VecFrameStack, SubprocVecEnv\n",
    "from stable_baselines3.common.callbacks import BaseCallback, EvalCallback\n",
    "from stable_baselines3.common.monitor import Monitor\n",
    "from stable_baselines3.common.env_checker import check_env\n",
    "from stable_baselines3.common.evaluation import evaluate_policy\n",
    "\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-12T20:08:10.909996280Z",
     "start_time": "2023-05-12T20:08:10.801531331Z"
    }
   },
   "outputs": [],
   "source": [
    "def softmax_normalization(actions):\n",
    "    numerator = np.exp(actions)\n",
    "    denominator = np.sum(np.exp(actions))\n",
    "    softmax_output = numerator/denominator\n",
    "    return softmax_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definition of gym Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# with open('data/sp500_it_components_7y.pickle', 'rb') as f:\n",
    "#     test_data = pickle.load(f)\n",
    "# test_data[0]['corrs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PortfolioAllocationEnv(gym.Env):\n",
    "    metadata = {'render.modes': ['human']}\n",
    "\n",
    "    def __init__(self, data_file,\n",
    "                 objective = 'max_return',\n",
    "                 risk_measure = 'MV',\n",
    "                 observations_range = (0, 1259)):\n",
    "        \"\"\"\n",
    "        Инициализация среды\n",
    "        \"\"\"\n",
    "        super(PortfolioAllocationEnv, self).__init__()\n",
    "\n",
    "        with open(data_file, 'rb') as f:\n",
    "            self.data = pickle.load(f)\n",
    "#             for d in self.data:\n",
    "#                 d['corrs'] = d['corrs'].iloc[3:, :]\n",
    "\n",
    "        self.stock_dim = self.data[0]['corrs'].shape[1]\n",
    "\n",
    "        close_price_changes = {stock['ticker']: stock['data']['Close_change'] for stock in self.data}\n",
    "        self.close_prices_pct = pd.DataFrame(close_price_changes)\n",
    "\n",
    "        self.tickers = [stock['ticker'] for stock in self.data]\n",
    "        self.n_assets = len(self.data)\n",
    "\n",
    "        self.objective = objective\n",
    "        self.risk_measure = risk_measure\n",
    "\n",
    "        self.rewards_memory = None\n",
    "        self.actions_memory = None\n",
    "\n",
    "        self.portfolio_value = 0\n",
    "        self.observations_range = observations_range\n",
    "        self.current_step = observations_range[0]\n",
    "\n",
    "        self.weights = np.ones(self.n_assets) / self.n_assets\n",
    "\n",
    "        self.reward_range = (-10, 10)\n",
    "        self.action_space = gym.spaces.Box(low=-1, high=1, shape=(self.n_assets,))\n",
    "        self.observation_space = gym.spaces.Box(low=-np.inf, high=np.inf, shape=(self.n_assets, self.stock_dim + 1))\n",
    "\n",
    "    def step(self, action):\n",
    "        \"\"\"\n",
    "        Выполнение одного шага\n",
    "        \"\"\"\n",
    "        # assert self.action_space.contains(action), \"Invalid action\"\n",
    "\n",
    "        # Получаем текущее состояние\n",
    "        state = self._get_state()\n",
    "\n",
    "        # Вычисляем награду\n",
    "        reward = self._get_reward(action)\n",
    "\n",
    "        # Обновляем текущий шаг\n",
    "        self.current_step += 1\n",
    "\n",
    "        # Проверяем, достигнут ли конец эпизода\n",
    "        done = self.current_step >= self.observations_range[1]\n",
    "\n",
    "        if done:\n",
    "            print(self.render())\n",
    "\n",
    "        # Возвращаем состояние, награду и флаг окончания эпизода\n",
    "        return state, reward, done, {}\n",
    "\n",
    "    def reset(self):\n",
    "        \"\"\"\n",
    "        Сброс среды в начальное состояние\n",
    "        \"\"\"\n",
    "        self.portfolio_value = 0\n",
    "        self.rewards_memory = []\n",
    "        self.current_step = self.observations_range[0]\n",
    "        self.actions_memory = []\n",
    "        self.weights = np.ones(self.n_assets) / self.n_assets\n",
    "        return self._get_state()\n",
    "\n",
    "    def render(self, mode='human'):\n",
    "        \"\"\"\n",
    "        Вывод информации о среде\n",
    "        \"\"\"\n",
    "        return {\n",
    "            'min_reward': min(self.rewards_memory),\n",
    "            'max_reward': max(self.rewards_memory),\n",
    "            'mean_reward': np.array(self.rewards_memory).mean(),\n",
    "            'portfolio_value': self.portfolio_value,\n",
    "            'weights_q0.25': np.quantile(self.weights, 0.25),\n",
    "            'weights_q0.5': np.quantile(self.weights, 0.5),\n",
    "            'weights_q0.75': np.quantile(self.weights, 0.75),\n",
    "            'weights_max': np.max(self.weights),\n",
    "            'weights_std': self.weights.std()\n",
    "        }\n",
    "\n",
    "    def _get_state(self):\n",
    "        \"\"\"\n",
    "        Получение текущего состояния\n",
    "        \"\"\"\n",
    "        values = [stock['corrs'].iloc[self.current_step].values for stock in self.data]\n",
    "        observation = np.vstack(values)\n",
    "        observation = np.concatenate([observation, self.weights[:, None]], axis=1).astype(np.float32)\n",
    "        observation = np.nan_to_num(observation)\n",
    "        return observation\n",
    "\n",
    "    def _get_reward(self, action):\n",
    "        \"\"\"\n",
    "        Вычисление награды\n",
    "        \"\"\"\n",
    "\n",
    "        self.weights += action\n",
    "        self.weights = softmax_normalization(self.weights)\n",
    "\n",
    "        self.actions_memory.append(action)\n",
    "\n",
    "        prices_change_hist = self.close_prices_pct.iloc[self.observations_range[0]:self.current_step + 1]\n",
    "        current_prices = self.close_prices_pct.iloc[self.current_step].values\n",
    "\n",
    "        portfolio_value_change = current_prices @ self.weights\n",
    "        self.portfolio_value += portfolio_value_change\n",
    "\n",
    "        reward = 0\n",
    "\n",
    "        if self.objective == 'sharpe':\n",
    "            mu = rf.mean_vector(prices_change_hist, method='hist')\n",
    "            cov = rf.covar_matrix(prices_change_hist, method='shrunk')\n",
    "            reward = rf.Sharpe(self.weights, mu, cov, prices_change_hist, rm=self.risk_measure)\n",
    "        elif self.objective == 'price_change':\n",
    "            # reward = portfolio_value_change\n",
    "            reward = portfolio_value_change\n",
    "        elif self.objective == 'min_risk':\n",
    "            mu = rf.mean_vector(prices_change_hist, method='hist')\n",
    "            cov = rf.covar_matrix(prices_change_hist, method='shrunk')\n",
    "            reward = -rf.Sharpe_Risk(self.weights, cov, mu, rm=self.risk_measure)\n",
    "\n",
    "        if np.isinf(reward):\n",
    "            reward = 0\n",
    "\n",
    "        self.rewards_memory.append(reward)\n",
    "        # print('act', self.current_step, np.isnan(action).sum(), np.array(self.rewards_memory).mean())\n",
    "\n",
    "        return reward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-13T01:07:32.129921055Z",
     "start_time": "2023-05-13T01:07:31.901781110Z"
    }
   },
   "outputs": [],
   "source": [
    "# class PortfolioAllocationEnv(gym.Env):\n",
    "#     metadata = {'render.modes': ['human']}\n",
    "\n",
    "#     def __init__(self, data_file,\n",
    "#                  sector = None,\n",
    "#                  objective = 'max_return',\n",
    "#                  risk_measure = 'MV',\n",
    "#                  drop_close = True,\n",
    "#                  observations_range = (0, 1259)):\n",
    "#         \"\"\"\n",
    "#         Инициализация среды\n",
    "#         \"\"\"\n",
    "#         super(PortfolioAllocationEnv, self).__init__()\n",
    "\n",
    "#         with open(data_file, 'rb') as f:\n",
    "#             self.data = pickle.load(f)\n",
    "#             if sector is not None:\n",
    "#                 self.data = list(filter(lambda stock: stock['sector'] == sector, self.data))\n",
    "#             if drop_close:\n",
    "#                 for stock in self.data:\n",
    "#                     stock['data'] = stock['data'].drop(['Close'], axis=1)\n",
    "\n",
    "#         self.stock_dim = self.data[0]['data'].shape[1]\n",
    "\n",
    "#         close_price_changes = {stock['ticker']: stock['data']['Close_Change'] for stock in self.data}\n",
    "#         self.close_prices_pct = pd.DataFrame(close_price_changes)\n",
    "\n",
    "#         self.tickers = [stock['ticker'] for stock in self.data]\n",
    "#         self.n_assets = len(self.data)\n",
    "\n",
    "#         self.objective = objective\n",
    "#         self.risk_measure = risk_measure\n",
    "\n",
    "#         self.rewards_memory = None\n",
    "#         self.actions_memory = None\n",
    "\n",
    "#         self.portfolio_value = 0\n",
    "#         self.observations_range = observations_range\n",
    "#         self.current_step = observations_range[0]\n",
    "\n",
    "#         self.weights = np.ones(self.n_assets) / self.n_assets\n",
    "\n",
    "#         self.reward_range = (-10, 10)\n",
    "#         self.action_space = gym.spaces.Box(low=-1, high=1, shape=(self.n_assets,))\n",
    "#         self.observation_space = gym.spaces.Box(low=-np.inf, high=np.inf, shape=(self.n_assets, self.stock_dim + 1))\n",
    "\n",
    "#     def step(self, action):\n",
    "#         \"\"\"\n",
    "#         Выполнение одного шага\n",
    "#         \"\"\"\n",
    "#         # assert self.action_space.contains(action), \"Invalid action\"\n",
    "\n",
    "#         # Получаем текущее состояние\n",
    "#         state = self._get_state()\n",
    "\n",
    "#         # Вычисляем награду\n",
    "#         reward = self._get_reward(action)\n",
    "\n",
    "#         # Обновляем текущий шаг\n",
    "#         self.current_step += 1\n",
    "\n",
    "#         # Проверяем, достигнут ли конец эпизода\n",
    "#         done = self.current_step >= self.observations_range[1]\n",
    "\n",
    "#         if done:\n",
    "#             print(self.render())\n",
    "\n",
    "#         # Возвращаем состояние, награду и флаг окончания эпизода\n",
    "#         return state, reward, done, {}\n",
    "\n",
    "#     def reset(self):\n",
    "#         \"\"\"\n",
    "#         Сброс среды в начальное состояние\n",
    "#         \"\"\"\n",
    "#         self.portfolio_value = 0\n",
    "#         self.rewards_memory = []\n",
    "#         self.current_step = self.observations_range[0]\n",
    "#         self.actions_memory = []\n",
    "#         self.weights = np.ones(self.n_assets) / self.n_assets\n",
    "#         return self._get_state()\n",
    "\n",
    "#     def render(self, mode='human'):\n",
    "#         \"\"\"\n",
    "#         Вывод информации о среде\n",
    "#         \"\"\"\n",
    "#         return {\n",
    "#             'min_reward': min(self.rewards_memory),\n",
    "#             'max_reward': max(self.rewards_memory),\n",
    "#             'mean_reward': np.array(self.rewards_memory).mean(),\n",
    "#             'portfolio_value': self.portfolio_value,\n",
    "#             'weights_q0.25': np.quantile(self.weights, 0.25),\n",
    "#             'weights_q0.5': np.quantile(self.weights, 0.5),\n",
    "#             'weights_q0.75': np.quantile(self.weights, 0.75),\n",
    "#             'weights_max': np.max(self.weights),\n",
    "#             'weights_std': self.weights.std()\n",
    "#         }\n",
    "\n",
    "#     def _get_state(self):\n",
    "#         \"\"\"\n",
    "#         Получение текущего состояния\n",
    "#         \"\"\"\n",
    "#         values = [stock['data'].iloc[self.current_step].values for stock in self.data]\n",
    "#         observation = np.vstack(values)\n",
    "#         observation = np.concatenate([observation, self.weights[:, None]], axis=1).astype(np.float32)\n",
    "#         observation = np.nan_to_num(observation)\n",
    "#         return observation\n",
    "\n",
    "#     def _get_reward(self, action):\n",
    "#         \"\"\"\n",
    "#         Вычисление награды\n",
    "#         \"\"\"\n",
    "\n",
    "#         self.weights += action\n",
    "#         self.weights = softmax_normalization(self.weights)\n",
    "\n",
    "#         self.actions_memory.append(action)\n",
    "\n",
    "#         prices_change_hist = self.close_prices_pct.iloc[self.observations_range[0]:self.current_step + 1]\n",
    "#         current_prices = self.close_prices_pct.iloc[self.current_step].values\n",
    "\n",
    "#         portfolio_value_change = current_prices @ self.weights\n",
    "#         self.portfolio_value += portfolio_value_change\n",
    "\n",
    "#         reward = 0\n",
    "\n",
    "#         if self.objective == 'sharpe':\n",
    "#             mu = rf.mean_vector(prices_change_hist, method='hist')\n",
    "#             cov = rf.covar_matrix(prices_change_hist, method='shrunk')\n",
    "#             reward = rf.Sharpe(self.weights, mu, cov, prices_change_hist, rm=self.risk_measure)\n",
    "#         elif self.objective == 'price_change':\n",
    "#             # reward = portfolio_value_change\n",
    "#             reward = portfolio_value_change\n",
    "#         elif self.objective == 'min_risk':\n",
    "#             mu = rf.mean_vector(prices_change_hist, method='hist')\n",
    "#             cov = rf.covar_matrix(prices_change_hist, method='shrunk')\n",
    "#             reward = -rf.Sharpe_Risk(self.weights, cov, mu, rm=self.risk_measure)\n",
    "\n",
    "#         if np.isinf(reward):\n",
    "#             reward = 0\n",
    "\n",
    "#         self.rewards_memory.append(reward)\n",
    "#         # print('act', self.current_step, np.isnan(action).sum(), np.array(self.rewards_memory).mean())\n",
    "\n",
    "#         return reward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-13T01:07:32.743801178Z",
     "start_time": "2023-05-13T01:07:32.557035306Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hivaze/Documents/CODE-W/PyCharm Projects/portfolio_optimization/venv/lib/python3.10/site-packages/stable_baselines3/common/env_checker.py:213: UserWarning: Your observation  has an unconventional shape (neither an image, nor a 1D vector). We recommend you to flatten the observation to have only a 1D vector or use a custom policy to properly process the data.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "train_env = PortfolioAllocationEnv(data_file='data/sp500_re_components_7y.pickle',\n",
    "#                                    sector='Information Technology',\n",
    "                                   objective='price_change',\n",
    "#                                    drop_close=False,\n",
    "                                   observations_range=(0, 1260))  # 252 x 5 = 1260, 252 x 3 = 756\n",
    "check_env(train_env, skip_render_check=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1760"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_env.data[0]['corrs'])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorization of Env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-13T01:07:33.153468948Z",
     "start_time": "2023-05-13T01:07:33.153126697Z"
    }
   },
   "outputs": [],
   "source": [
    "from stable_baselines3.common.vec_env import VecCheckNan\n",
    "\n",
    "train_env = DummyVecEnv([lambda: train_env])\n",
    "train_env = VecNormalize(train_env, norm_obs=True, norm_reward=False)\n",
    "train_env = VecCheckNan(train_env, raise_exception=True)\n",
    "# train_env = VecFrameStack(train_env, 15, channels_order='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-13T01:07:33.583955244Z",
     "start_time": "2023-05-13T01:07:33.569749207Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((28, 41), (28,))"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_env.observation_space.shape, train_env.action_space.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## stablebaselines Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-13T01:07:59.265525763Z",
     "start_time": "2023-05-13T01:07:59.060669140Z"
    }
   },
   "outputs": [],
   "source": [
    "from stable_baselines3.ppo import MlpPolicy\n",
    "from sb3_contrib.ppo_recurrent import MlpLstmPolicy\n",
    "from sb3_contrib import RecurrentPPO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "DDPG?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-13T01:07:59.863375889Z",
     "start_time": "2023-05-13T01:07:59.675134938Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cuda device\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SACPolicy(\n",
       "  (actor): Actor(\n",
       "    (features_extractor): FlattenExtractor(\n",
       "      (flatten): Flatten(start_dim=1, end_dim=-1)\n",
       "    )\n",
       "    (latent_pi): Sequential(\n",
       "      (0): Linear(in_features=1148, out_features=256, bias=True)\n",
       "      (1): ReLU()\n",
       "      (2): Linear(in_features=256, out_features=256, bias=True)\n",
       "      (3): ReLU()\n",
       "    )\n",
       "    (mu): Linear(in_features=256, out_features=28, bias=True)\n",
       "    (log_std): Linear(in_features=256, out_features=28, bias=True)\n",
       "  )\n",
       "  (critic): ContinuousCritic(\n",
       "    (features_extractor): FlattenExtractor(\n",
       "      (flatten): Flatten(start_dim=1, end_dim=-1)\n",
       "    )\n",
       "    (qf0): Sequential(\n",
       "      (0): Linear(in_features=1176, out_features=256, bias=True)\n",
       "      (1): ReLU()\n",
       "      (2): Linear(in_features=256, out_features=256, bias=True)\n",
       "      (3): ReLU()\n",
       "      (4): Linear(in_features=256, out_features=1, bias=True)\n",
       "    )\n",
       "    (qf1): Sequential(\n",
       "      (0): Linear(in_features=1176, out_features=256, bias=True)\n",
       "      (1): ReLU()\n",
       "      (2): Linear(in_features=256, out_features=256, bias=True)\n",
       "      (3): ReLU()\n",
       "      (4): Linear(in_features=256, out_features=1, bias=True)\n",
       "    )\n",
       "  )\n",
       "  (critic_target): ContinuousCritic(\n",
       "    (features_extractor): FlattenExtractor(\n",
       "      (flatten): Flatten(start_dim=1, end_dim=-1)\n",
       "    )\n",
       "    (qf0): Sequential(\n",
       "      (0): Linear(in_features=1176, out_features=256, bias=True)\n",
       "      (1): ReLU()\n",
       "      (2): Linear(in_features=256, out_features=256, bias=True)\n",
       "      (3): ReLU()\n",
       "      (4): Linear(in_features=256, out_features=1, bias=True)\n",
       "    )\n",
       "    (qf1): Sequential(\n",
       "      (0): Linear(in_features=1176, out_features=256, bias=True)\n",
       "      (1): ReLU()\n",
       "      (2): Linear(in_features=256, out_features=256, bias=True)\n",
       "      (3): ReLU()\n",
       "      (4): Linear(in_features=256, out_features=1, bias=True)\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SAC('MlpPolicy', train_env, learning_rate=1e-3, buffer_size=20_000,\n",
    "            verbose=1, tensorboard_log='logs/',\n",
    "            device='cuda', batch_size=128,\n",
    "#             policy_kwargs={'net_arch': [800, 400]}\n",
    "            )\n",
    "model.policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-13T01:08:04.656986004Z",
     "start_time": "2023-05-13T01:08:00.524492883Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'min_reward': -0.17888513111263077, 'max_reward': 0.1016405990048536, 'mean_reward': 0.00027419362430891305, 'portfolio_value': 0.3454839666292308, 'weights_q0.25': 0.019981438691910217, 'weights_q0.5': 0.025651049657377967, 'weights_q0.75': 0.0489902893783318, 'weights_max': 0.09459255277172963, 'weights_std': 0.022267354644032733}\n",
      "[0.3454839686530704] [1260]\n"
     ]
    }
   ],
   "source": [
    "mean_reward, std_reward = evaluate_policy(model, train_env, n_eval_episodes=1, deterministic=False, warn=False, return_episode_rewards=True)\n",
    "print(mean_reward, std_reward)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learn loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-13T01:08:04.663843078Z",
     "start_time": "2023-05-13T01:08:04.657151773Z"
    }
   },
   "outputs": [],
   "source": [
    "# eval_callback = EvalCallback(eval_env=test_env, best_model_save_path='models/best.stbl',\n",
    "#                              n_eval_episodes=3, eval_freq=500, render=False, warn=False)\n",
    "# eval_callback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-13T01:08:04.892095500Z",
     "start_time": "2023-05-13T01:08:04.663740382Z"
    }
   },
   "outputs": [],
   "source": [
    "class MeanRewardCallback(BaseCallback):\n",
    "    def __init__(self, verbose=0):\n",
    "        super(MeanRewardCallback, self).__init__(verbose)\n",
    "        self.render = {}\n",
    "\n",
    "    def _on_step(self) -> bool:\n",
    "\n",
    "        if self.training_env.get_attr('rewards_memory')[0]:  # else - end of episode\n",
    "            # reward = self.training_env.get_attr('rewards_memory')[0][-1]\n",
    "            self.render = self.training_env.render()\n",
    "        else:\n",
    "            self.logger.record(\"train/mean_reward\", self.render['mean_reward'])\n",
    "            self.logger.record(\"train/min_reward\", self.render['min_reward'])\n",
    "            self.logger.record(\"train/max_reward\", self.render['max_reward'])\n",
    "            self.logger.record(\"train/portfolio_value\", self.render['portfolio_value'])\n",
    "\n",
    "        return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2023-05-13T01:08:30.293921566Z"
    },
    "is_executing": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logging to logs/SAC-mr-re-256-256-corrs-dw_0\n",
      "{'min_reward': -0.1788921409979694, 'max_reward': 0.08892297520103949, 'mean_reward': 0.0002711987671513675, 'portfolio_value': 0.3417104466107232, 'weights_q0.25': 0.020148079638662754, 'weights_q0.5': 0.031008298210897442, 'weights_q0.75': 0.05254317331350907, 'weights_max': 0.08901289524707764, 'weights_std': 0.01875007540657821}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 1        |\n",
      "|    fps             | 148      |\n",
      "|    time_elapsed    | 8        |\n",
      "|    total_timesteps | 1260     |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -47.3    |\n",
      "|    critic_loss     | 0.46     |\n",
      "|    ent_coef        | 0.315    |\n",
      "|    ent_coef_loss   | -53.6    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.0889   |\n",
      "|    mean_reward     | 0.000266 |\n",
      "|    min_reward      | -0.179   |\n",
      "|    n_updates       | 1159     |\n",
      "|    portfolio_value | 0.335    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.17776950402654695, 'max_reward': 0.09594755746237613, 'mean_reward': 0.00027559970710009887, 'portfolio_value': 0.34725563094612444, 'weights_q0.25': 0.015876506494467313, 'weights_q0.5': 0.02382157808848163, 'weights_q0.75': 0.05469292037239775, 'weights_max': 0.07497357363768019, 'weights_std': 0.021401540583173163}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 2        |\n",
      "|    fps             | 140      |\n",
      "|    time_elapsed    | 17       |\n",
      "|    total_timesteps | 2520     |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -53.4    |\n",
      "|    critic_loss     | 0.634    |\n",
      "|    ent_coef        | 0.0908   |\n",
      "|    ent_coef_loss   | -107     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.0959   |\n",
      "|    mean_reward     | 0.000272 |\n",
      "|    min_reward      | -0.178   |\n",
      "|    n_updates       | 2419     |\n",
      "|    portfolio_value | 0.342    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16567815796731344, 'max_reward': 0.09386553503207194, 'mean_reward': 0.0002772861494403835, 'portfolio_value': 0.34938054829488324, 'weights_q0.25': 0.01866882873871612, 'weights_q0.5': 0.031910091326463684, 'weights_q0.75': 0.050676020022143095, 'weights_max': 0.0699057539823709, 'weights_std': 0.019453614499902367}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 3        |\n",
      "|    fps             | 138      |\n",
      "|    time_elapsed    | 27       |\n",
      "|    total_timesteps | 3780     |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -53.1    |\n",
      "|    critic_loss     | 1.48     |\n",
      "|    ent_coef        | 0.0269   |\n",
      "|    ent_coef_loss   | -148     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.0939   |\n",
      "|    mean_reward     | 0.000272 |\n",
      "|    min_reward      | -0.166   |\n",
      "|    n_updates       | 3679     |\n",
      "|    portfolio_value | 0.342    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.17661005439688676, 'max_reward': 0.0968577871876242, 'mean_reward': 0.00023518424217796447, 'portfolio_value': 0.29633214514423545, 'weights_q0.25': 0.011773713211947555, 'weights_q0.5': 0.03832457035834002, 'weights_q0.75': 0.05878331377039516, 'weights_max': 0.06657975259321194, 'weights_std': 0.02280895475053205}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 4        |\n",
      "|    fps             | 136      |\n",
      "|    time_elapsed    | 37       |\n",
      "|    total_timesteps | 5040     |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -49.8    |\n",
      "|    critic_loss     | 0.169    |\n",
      "|    ent_coef        | 0.0086   |\n",
      "|    ent_coef_loss   | -169     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.0969   |\n",
      "|    mean_reward     | 0.000231 |\n",
      "|    min_reward      | -0.177   |\n",
      "|    n_updates       | 4939     |\n",
      "|    portfolio_value | 0.291    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.1642885630744203, 'max_reward': 0.08957280299616173, 'mean_reward': 0.00027495162272798436, 'portfolio_value': 0.34643904463726, 'weights_q0.25': 0.010541012994916672, 'weights_q0.5': 0.04046331202581439, 'weights_q0.75': 0.054021169345830615, 'weights_max': 0.06554679934037765, 'weights_std': 0.021393175852069752}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 5        |\n",
      "|    fps             | 135      |\n",
      "|    time_elapsed    | 46       |\n",
      "|    total_timesteps | 6300     |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -47.7    |\n",
      "|    critic_loss     | 0.116    |\n",
      "|    ent_coef        | 0.00315  |\n",
      "|    ent_coef_loss   | -117     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.0896   |\n",
      "|    mean_reward     | 0.000266 |\n",
      "|    min_reward      | -0.164   |\n",
      "|    n_updates       | 6199     |\n",
      "|    portfolio_value | 0.335    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.17685526258052509, 'max_reward': 0.10071327107730713, 'mean_reward': 0.00028355453744874457, 'portfolio_value': 0.35727871718541837, 'weights_q0.25': 0.01540131271081177, 'weights_q0.5': 0.033950344236984786, 'weights_q0.75': 0.05217270379689902, 'weights_max': 0.06905003694402949, 'weights_std': 0.019908963758010496}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 6        |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 56       |\n",
      "|    total_timesteps | 7560     |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -44.5    |\n",
      "|    critic_loss     | 0.0847   |\n",
      "|    ent_coef        | 0.00149  |\n",
      "|    ent_coef_loss   | -57.1    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.101    |\n",
      "|    mean_reward     | 0.000276 |\n",
      "|    min_reward      | -0.177   |\n",
      "|    n_updates       | 7459     |\n",
      "|    portfolio_value | 0.347    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16607311880009984, 'max_reward': 0.09411526224584453, 'mean_reward': 0.00021708746595614872, 'portfolio_value': 0.2735302071047473, 'weights_q0.25': 0.014331516973116586, 'weights_q0.5': 0.022847301257500497, 'weights_q0.75': 0.06210009507285814, 'weights_max': 0.07196777129124604, 'weights_std': 0.023700947321897994}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 7        |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 65       |\n",
      "|    total_timesteps | 8820     |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -43.2    |\n",
      "|    critic_loss     | 0.165    |\n",
      "|    ent_coef        | 0.000955 |\n",
      "|    ent_coef_loss   | -33.6    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.0941   |\n",
      "|    mean_reward     | 0.000211 |\n",
      "|    min_reward      | -0.166   |\n",
      "|    n_updates       | 8719     |\n",
      "|    portfolio_value | 0.265    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.1780167657898209, 'max_reward': 0.128975039317278, 'mean_reward': 0.00031517427640407805, 'portfolio_value': 0.39711958826913807, 'weights_q0.25': 0.01430584755110123, 'weights_q0.5': 0.026706126073871597, 'weights_q0.75': 0.053805655977679716, 'weights_max': 0.07094407431370423, 'weights_std': 0.021958210662545715}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 8        |\n",
      "|    fps             | 133      |\n",
      "|    time_elapsed    | 75       |\n",
      "|    total_timesteps | 10080    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -40.4    |\n",
      "|    critic_loss     | 0.0302   |\n",
      "|    ent_coef        | 0.000775 |\n",
      "|    ent_coef_loss   | -36.5    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.129    |\n",
      "|    mean_reward     | 0.000305 |\n",
      "|    min_reward      | -0.178   |\n",
      "|    n_updates       | 9979     |\n",
      "|    portfolio_value | 0.384    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.17522516734836246, 'max_reward': 0.09221875400170165, 'mean_reward': 0.00024887724098327673, 'portfolio_value': 0.3135853236389283, 'weights_q0.25': 0.012559004471986226, 'weights_q0.5': 0.026235073589073055, 'weights_q0.75': 0.058989439713415956, 'weights_max': 0.06678640111443437, 'weights_std': 0.022046936890473108}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 9        |\n",
      "|    fps             | 133      |\n",
      "|    time_elapsed    | 84       |\n",
      "|    total_timesteps | 11340    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -35.5    |\n",
      "|    critic_loss     | 0.244    |\n",
      "|    ent_coef        | 0.0006   |\n",
      "|    ent_coef_loss   | -7.45    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.0922   |\n",
      "|    mean_reward     | 0.000241 |\n",
      "|    min_reward      | -0.175   |\n",
      "|    n_updates       | 11239    |\n",
      "|    portfolio_value | 0.303    |\n",
      "---------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'min_reward': -0.17885128215343749, 'max_reward': 0.1259980923644234, 'mean_reward': 0.00029960163746818743, 'portfolio_value': 0.3774980632099162, 'weights_q0.25': 0.01502203438845991, 'weights_q0.5': 0.03062525712245362, 'weights_q0.75': 0.05189125820344894, 'weights_max': 0.07333904550270333, 'weights_std': 0.022123288943855696}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 10       |\n",
      "|    fps             | 133      |\n",
      "|    time_elapsed    | 94       |\n",
      "|    total_timesteps | 12600    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -33.3    |\n",
      "|    critic_loss     | 0.051    |\n",
      "|    ent_coef        | 0.000514 |\n",
      "|    ent_coef_loss   | 5.26     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.126    |\n",
      "|    mean_reward     | 0.00029  |\n",
      "|    min_reward      | -0.179   |\n",
      "|    n_updates       | 12499    |\n",
      "|    portfolio_value | 0.365    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16383123820120393, 'max_reward': 0.09945964230866391, 'mean_reward': 0.00028575798836815813, 'portfolio_value': 0.3600550653438795, 'weights_q0.25': 0.01626869566724784, 'weights_q0.5': 0.03429609347619793, 'weights_q0.75': 0.0538708346766605, 'weights_max': 0.06460970614696802, 'weights_std': 0.019065402353665994}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 11       |\n",
      "|    fps             | 133      |\n",
      "|    time_elapsed    | 103      |\n",
      "|    total_timesteps | 13860    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -33      |\n",
      "|    critic_loss     | 0.0298   |\n",
      "|    ent_coef        | 0.000424 |\n",
      "|    ent_coef_loss   | -18.3    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.0995   |\n",
      "|    mean_reward     | 0.000281 |\n",
      "|    min_reward      | -0.164   |\n",
      "|    n_updates       | 13759    |\n",
      "|    portfolio_value | 0.353    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.1703766097306515, 'max_reward': 0.0968123386933683, 'mean_reward': 0.00028573847410702394, 'portfolio_value': 0.36003047737485, 'weights_q0.25': 0.01195283838302446, 'weights_q0.5': 0.03690837396625492, 'weights_q0.75': 0.05819514629228399, 'weights_max': 0.07153077754625262, 'weights_std': 0.02159133559653328}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 12       |\n",
      "|    fps             | 133      |\n",
      "|    time_elapsed    | 113      |\n",
      "|    total_timesteps | 15120    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -30.2    |\n",
      "|    critic_loss     | 0.00637  |\n",
      "|    ent_coef        | 0.000295 |\n",
      "|    ent_coef_loss   | -1.96    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.0968   |\n",
      "|    mean_reward     | 0.000281 |\n",
      "|    min_reward      | -0.17    |\n",
      "|    n_updates       | 15019    |\n",
      "|    portfolio_value | 0.354    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.167507510208878, 'max_reward': 0.08428488825870914, 'mean_reward': 0.0002540110739531483, 'portfolio_value': 0.3200539531809672, 'weights_q0.25': 0.022292500624570392, 'weights_q0.5': 0.030906488429811238, 'weights_q0.75': 0.047742220604502894, 'weights_max': 0.10804451095600298, 'weights_std': 0.019788897465081626}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 13       |\n",
      "|    fps             | 133      |\n",
      "|    time_elapsed    | 123      |\n",
      "|    total_timesteps | 16380    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -26.9    |\n",
      "|    critic_loss     | 0.00842  |\n",
      "|    ent_coef        | 0.00037  |\n",
      "|    ent_coef_loss   | -82.6    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.0843   |\n",
      "|    mean_reward     | 0.000251 |\n",
      "|    min_reward      | -0.168   |\n",
      "|    n_updates       | 16279    |\n",
      "|    portfolio_value | 0.316    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.1775120025211414, 'max_reward': 0.11352773781429978, 'mean_reward': 0.00027314428179070575, 'portfolio_value': 0.34416179505628886, 'weights_q0.25': 0.02075944457532508, 'weights_q0.5': 0.031113523517075137, 'weights_q0.75': 0.05098078462077388, 'weights_max': 0.07064996946967686, 'weights_std': 0.01780995179140179}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 14       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 132      |\n",
      "|    total_timesteps | 17640    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -26      |\n",
      "|    critic_loss     | 0.122    |\n",
      "|    ent_coef        | 0.000302 |\n",
      "|    ent_coef_loss   | 1.68     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.114    |\n",
      "|    mean_reward     | 0.00027  |\n",
      "|    min_reward      | -0.178   |\n",
      "|    n_updates       | 17539    |\n",
      "|    portfolio_value | 0.34     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.18077467555274185, 'max_reward': 0.11737016693181906, 'mean_reward': 0.00016034533340228164, 'portfolio_value': 0.20203512008687446, 'weights_q0.25': 0.016743625514873024, 'weights_q0.5': 0.030670175246381045, 'weights_q0.75': 0.05340858217599656, 'weights_max': 0.07169013628818219, 'weights_std': 0.019829494544616893}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 15       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 142      |\n",
      "|    total_timesteps | 18900    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -23.9    |\n",
      "|    critic_loss     | 0.0191   |\n",
      "|    ent_coef        | 0.000286 |\n",
      "|    ent_coef_loss   | -33.4    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.117    |\n",
      "|    mean_reward     | 0.000159 |\n",
      "|    min_reward      | -0.181   |\n",
      "|    n_updates       | 18799    |\n",
      "|    portfolio_value | 0.201    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16907073575639142, 'max_reward': 0.10872177818117859, 'mean_reward': 0.00023477694572861943, 'portfolio_value': 0.2958189516180602, 'weights_q0.25': 0.022252817076532, 'weights_q0.5': 0.031019177344093936, 'weights_q0.75': 0.0477526255242322, 'weights_max': 0.07804443762598025, 'weights_std': 0.017738317229098282}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 16       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 151      |\n",
      "|    total_timesteps | 20160    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -23.2    |\n",
      "|    critic_loss     | 0.0129   |\n",
      "|    ent_coef        | 0.000271 |\n",
      "|    ent_coef_loss   | -1.65    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.109    |\n",
      "|    mean_reward     | 0.000225 |\n",
      "|    min_reward      | -0.169   |\n",
      "|    n_updates       | 20059    |\n",
      "|    portfolio_value | 0.283    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.17997479595109458, 'max_reward': 0.10072190037236306, 'mean_reward': 0.00033971614493506254, 'portfolio_value': 0.4280423426181789, 'weights_q0.25': 0.02050605325051722, 'weights_q0.5': 0.02469789751837893, 'weights_q0.75': 0.05521122360688417, 'weights_max': 0.08111776030495897, 'weights_std': 0.02102403861527365}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 17       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 161      |\n",
      "|    total_timesteps | 21420    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -21.7    |\n",
      "|    critic_loss     | 0.00654  |\n",
      "|    ent_coef        | 0.000162 |\n",
      "|    ent_coef_loss   | 39.6     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.101    |\n",
      "|    mean_reward     | 0.000331 |\n",
      "|    min_reward      | -0.18    |\n",
      "|    n_updates       | 21319    |\n",
      "|    portfolio_value | 0.416    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.17159943302554048, 'max_reward': 0.12620310652029593, 'mean_reward': 0.00031945357125611834, 'portfolio_value': 0.4025114997827091, 'weights_q0.25': 0.021024053942492574, 'weights_q0.5': 0.03305607912499124, 'weights_q0.75': 0.04736889074923219, 'weights_max': 0.07252321435955687, 'weights_std': 0.017192345513482393}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 18       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 170      |\n",
      "|    total_timesteps | 22680    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -20.5    |\n",
      "|    critic_loss     | 0.0119   |\n",
      "|    ent_coef        | 0.00025  |\n",
      "|    ent_coef_loss   | -0.316   |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.126    |\n",
      "|    mean_reward     | 0.000311 |\n",
      "|    min_reward      | -0.172   |\n",
      "|    n_updates       | 22579    |\n",
      "|    portfolio_value | 0.392    |\n",
      "---------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'min_reward': -0.17069456722223045, 'max_reward': 0.08590975509671253, 'mean_reward': 0.0003268243721808611, 'portfolio_value': 0.4117987089478838, 'weights_q0.25': 0.0252067170377713, 'weights_q0.5': 0.03415465150326273, 'weights_q0.75': 0.0410142446586077, 'weights_max': 0.06916042662412752, 'weights_std': 0.015457435630642398}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 19       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 180      |\n",
      "|    total_timesteps | 23940    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -17.3    |\n",
      "|    critic_loss     | 0.011    |\n",
      "|    ent_coef        | 0.000226 |\n",
      "|    ent_coef_loss   | -12.8    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.0859   |\n",
      "|    mean_reward     | 0.000321 |\n",
      "|    min_reward      | -0.171   |\n",
      "|    n_updates       | 23839    |\n",
      "|    portfolio_value | 0.404    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.18017314337973067, 'max_reward': 0.10413761744526664, 'mean_reward': 0.00025756308124854244, 'portfolio_value': 0.3245294823731636, 'weights_q0.25': 0.02264912476856341, 'weights_q0.5': 0.030542603121023696, 'weights_q0.75': 0.04543163545267782, 'weights_max': 0.07587538477469072, 'weights_std': 0.016265278860420725}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 20       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 189      |\n",
      "|    total_timesteps | 25200    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -17.6    |\n",
      "|    critic_loss     | 0.00198  |\n",
      "|    ent_coef        | 0.000151 |\n",
      "|    ent_coef_loss   | -61.9    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.104    |\n",
      "|    mean_reward     | 0.000252 |\n",
      "|    min_reward      | -0.18    |\n",
      "|    n_updates       | 25099    |\n",
      "|    portfolio_value | 0.317    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.17208151940712194, 'max_reward': 0.11147726058675778, 'mean_reward': 0.00033319903119317663, 'portfolio_value': 0.4198307793034029, 'weights_q0.25': 0.02368491180267775, 'weights_q0.5': 0.03418401450682587, 'weights_q0.75': 0.04992182597678794, 'weights_max': 0.06297536385646157, 'weights_std': 0.015156241309790527}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 21       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 199      |\n",
      "|    total_timesteps | 26460    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -15.8    |\n",
      "|    critic_loss     | 0.00164  |\n",
      "|    ent_coef        | 0.000136 |\n",
      "|    ent_coef_loss   | -27.2    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.111    |\n",
      "|    mean_reward     | 0.000326 |\n",
      "|    min_reward      | -0.172   |\n",
      "|    n_updates       | 26359    |\n",
      "|    portfolio_value | 0.411    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.17568006172851147, 'max_reward': 0.1297642469795704, 'mean_reward': 0.0002861133576873742, 'portfolio_value': 0.36050283068609107, 'weights_q0.25': 0.020491485228405548, 'weights_q0.5': 0.030096536239338514, 'weights_q0.75': 0.047163904036050086, 'weights_max': 0.07504056977511453, 'weights_std': 0.016982510456461417}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 22       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 208      |\n",
      "|    total_timesteps | 27720    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -13.8    |\n",
      "|    critic_loss     | 0.00147  |\n",
      "|    ent_coef        | 0.000113 |\n",
      "|    ent_coef_loss   | -40.9    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.13     |\n",
      "|    mean_reward     | 0.00028  |\n",
      "|    min_reward      | -0.176   |\n",
      "|    n_updates       | 27619    |\n",
      "|    portfolio_value | 0.352    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.180410045426663, 'max_reward': 0.09912433499762066, 'mean_reward': 0.0002493099792288982, 'portfolio_value': 0.3141305738284116, 'weights_q0.25': 0.021612835946821424, 'weights_q0.5': 0.03463043329672876, 'weights_q0.75': 0.04797463928221666, 'weights_max': 0.0648266761751099, 'weights_std': 0.015305041849751944}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 23       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 218      |\n",
      "|    total_timesteps | 28980    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -13.2    |\n",
      "|    critic_loss     | 0.00161  |\n",
      "|    ent_coef        | 0.000137 |\n",
      "|    ent_coef_loss   | -75.3    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.0991   |\n",
      "|    mean_reward     | 0.000243 |\n",
      "|    min_reward      | -0.18    |\n",
      "|    n_updates       | 28879    |\n",
      "|    portfolio_value | 0.305    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.1752098252425187, 'max_reward': 0.13387384462674545, 'mean_reward': 0.00029119166008607813, 'portfolio_value': 0.36690149170845887, 'weights_q0.25': 0.024661645957613165, 'weights_q0.5': 0.03204790465555773, 'weights_q0.75': 0.046649005282086764, 'weights_max': 0.06633334526128115, 'weights_std': 0.01503064522048948}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 24       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 227      |\n",
      "|    total_timesteps | 30240    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -12.8    |\n",
      "|    critic_loss     | 0.00447  |\n",
      "|    ent_coef        | 9.57e-05 |\n",
      "|    ent_coef_loss   | -15.9    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.134    |\n",
      "|    mean_reward     | 0.000286 |\n",
      "|    min_reward      | -0.175   |\n",
      "|    n_updates       | 30139    |\n",
      "|    portfolio_value | 0.36     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.17343264115504817, 'max_reward': 0.10816000964059043, 'mean_reward': 0.00034212823939993735, 'portfolio_value': 0.43108158164392085, 'weights_q0.25': 0.02448052471829714, 'weights_q0.5': 0.03172525177123617, 'weights_q0.75': 0.0432277831299036, 'weights_max': 0.06881715728080376, 'weights_std': 0.016106150566947707}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 25       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 237      |\n",
      "|    total_timesteps | 31500    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -12.2    |\n",
      "|    critic_loss     | 0.00187  |\n",
      "|    ent_coef        | 9.68e-05 |\n",
      "|    ent_coef_loss   | -19.9    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.108    |\n",
      "|    mean_reward     | 0.000338 |\n",
      "|    min_reward      | -0.173   |\n",
      "|    n_updates       | 31399    |\n",
      "|    portfolio_value | 0.426    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.17881555327044596, 'max_reward': 0.14533142944265892, 'mean_reward': 0.00023314717984991925, 'portfolio_value': 0.2937654466108985, 'weights_q0.25': 0.018827073971038617, 'weights_q0.5': 0.02927606154982903, 'weights_q0.75': 0.05160993355624802, 'weights_max': 0.07886598585853839, 'weights_std': 0.02087682469419661}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 26       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 246      |\n",
      "|    total_timesteps | 32760    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -11.6    |\n",
      "|    critic_loss     | 0.000859 |\n",
      "|    ent_coef        | 7.33e-05 |\n",
      "|    ent_coef_loss   | -16.8    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.145    |\n",
      "|    mean_reward     | 0.00023  |\n",
      "|    min_reward      | -0.179   |\n",
      "|    n_updates       | 32659    |\n",
      "|    portfolio_value | 0.29     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.18260840471316375, 'max_reward': 0.1566893581181506, 'mean_reward': 0.00026053322200664817, 'portfolio_value': 0.3282718597283767, 'weights_q0.25': 0.02112663762716304, 'weights_q0.5': 0.03124859947161717, 'weights_q0.75': 0.04444806127272026, 'weights_max': 0.07209054705985635, 'weights_std': 0.01819277097985851}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 27       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 256      |\n",
      "|    total_timesteps | 34020    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -10.5    |\n",
      "|    critic_loss     | 0.000719 |\n",
      "|    ent_coef        | 6.52e-05 |\n",
      "|    ent_coef_loss   | -63.5    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.157    |\n",
      "|    mean_reward     | 0.000256 |\n",
      "|    min_reward      | -0.183   |\n",
      "|    n_updates       | 33919    |\n",
      "|    portfolio_value | 0.322    |\n",
      "---------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'min_reward': -0.17865659519297905, 'max_reward': 0.14562616676973522, 'mean_reward': 0.0003682611643490276, 'portfolio_value': 0.46400906707977524, 'weights_q0.25': 0.02045672453129592, 'weights_q0.5': 0.035595033270397064, 'weights_q0.75': 0.04859479027766392, 'weights_max': 0.06774716648036291, 'weights_std': 0.0174308447847073}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 28       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 265      |\n",
      "|    total_timesteps | 35280    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -9.6     |\n",
      "|    critic_loss     | 0.000623 |\n",
      "|    ent_coef        | 7.59e-05 |\n",
      "|    ent_coef_loss   | -63.4    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.146    |\n",
      "|    mean_reward     | 0.000359 |\n",
      "|    min_reward      | -0.179   |\n",
      "|    n_updates       | 35179    |\n",
      "|    portfolio_value | 0.452    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.1797662494113489, 'max_reward': 0.1373023205009341, 'mean_reward': 0.00032652776554094727, 'portfolio_value': 0.41142498458159316, 'weights_q0.25': 0.022838573105382638, 'weights_q0.5': 0.030289550704986117, 'weights_q0.75': 0.05069767306423671, 'weights_max': 0.06729319026886857, 'weights_std': 0.015980155459624782}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 29       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 275      |\n",
      "|    total_timesteps | 36540    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -9.32    |\n",
      "|    critic_loss     | 0.00151  |\n",
      "|    ent_coef        | 5.12e-05 |\n",
      "|    ent_coef_loss   | -44.5    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.137    |\n",
      "|    mean_reward     | 0.000319 |\n",
      "|    min_reward      | -0.18    |\n",
      "|    n_updates       | 36439    |\n",
      "|    portfolio_value | 0.401    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.18253311499736227, 'max_reward': 0.17603082304181417, 'mean_reward': 0.0003536813997779294, 'portfolio_value': 0.4456385637201914, 'weights_q0.25': 0.02554732210978371, 'weights_q0.5': 0.03321582411322796, 'weights_q0.75': 0.04208429345190583, 'weights_max': 0.06970764275161868, 'weights_std': 0.016397566180146712}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 30       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 284      |\n",
      "|    total_timesteps | 37800    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -8.6     |\n",
      "|    critic_loss     | 0.00105  |\n",
      "|    ent_coef        | 5.08e-05 |\n",
      "|    ent_coef_loss   | 106      |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.176    |\n",
      "|    mean_reward     | 0.000348 |\n",
      "|    min_reward      | -0.183   |\n",
      "|    n_updates       | 37699    |\n",
      "|    portfolio_value | 0.438    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.1780246584034298, 'max_reward': 0.1410809006191295, 'mean_reward': 0.00042320135704972854, 'portfolio_value': 0.5332337098826577, 'weights_q0.25': 0.020189834508879585, 'weights_q0.5': 0.030336519714738136, 'weights_q0.75': 0.048356383223541924, 'weights_max': 0.07563065186642966, 'weights_std': 0.018574165060274032}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 31       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 294      |\n",
      "|    total_timesteps | 39060    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -7.41    |\n",
      "|    critic_loss     | 0.00213  |\n",
      "|    ent_coef        | 4.76e-05 |\n",
      "|    ent_coef_loss   | 17.7     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.141    |\n",
      "|    mean_reward     | 0.000418 |\n",
      "|    min_reward      | -0.178   |\n",
      "|    n_updates       | 38959    |\n",
      "|    portfolio_value | 0.527    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.17043005120506605, 'max_reward': 0.13571417203725267, 'mean_reward': 0.000452798297510152, 'portfolio_value': 0.5705258548627913, 'weights_q0.25': 0.020327708337270448, 'weights_q0.5': 0.03338812573625921, 'weights_q0.75': 0.05036219902465708, 'weights_max': 0.06681998419301446, 'weights_std': 0.015847170389743332}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 32       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 303      |\n",
      "|    total_timesteps | 40320    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -7.23    |\n",
      "|    critic_loss     | 0.000525 |\n",
      "|    ent_coef        | 4.56e-05 |\n",
      "|    ent_coef_loss   | -14.6    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.136    |\n",
      "|    mean_reward     | 0.000446 |\n",
      "|    min_reward      | -0.17    |\n",
      "|    n_updates       | 40219    |\n",
      "|    portfolio_value | 0.561    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.1701870361836503, 'max_reward': 0.1599153666953267, 'mean_reward': 0.0004155272998195134, 'portfolio_value': 0.5235643977725866, 'weights_q0.25': 0.022528531039699616, 'weights_q0.5': 0.03194717460372682, 'weights_q0.75': 0.0496849539328672, 'weights_max': 0.0662773881421025, 'weights_std': 0.015570453902080084}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 33       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 313      |\n",
      "|    total_timesteps | 41580    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -6.87    |\n",
      "|    critic_loss     | 0.000376 |\n",
      "|    ent_coef        | 4.01e-05 |\n",
      "|    ent_coef_loss   | -38.3    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.16     |\n",
      "|    mean_reward     | 0.00041  |\n",
      "|    min_reward      | -0.17    |\n",
      "|    n_updates       | 41479    |\n",
      "|    portfolio_value | 0.516    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.18448701378805094, 'max_reward': 0.17248144559287304, 'mean_reward': 0.0004466997614095702, 'portfolio_value': 0.5628416993760579, 'weights_q0.25': 0.02311136697673262, 'weights_q0.5': 0.03663173470054805, 'weights_q0.75': 0.045536476925979424, 'weights_max': 0.06785512937965064, 'weights_std': 0.014917274255871133}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 34       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 322      |\n",
      "|    total_timesteps | 42840    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -5.94    |\n",
      "|    critic_loss     | 0.000171 |\n",
      "|    ent_coef        | 4.1e-05  |\n",
      "|    ent_coef_loss   | -112     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.172    |\n",
      "|    mean_reward     | 0.00044  |\n",
      "|    min_reward      | -0.184   |\n",
      "|    n_updates       | 42739    |\n",
      "|    portfolio_value | 0.554    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.184218627778361, 'max_reward': 0.09720943119562102, 'mean_reward': 0.00038075035440200656, 'portfolio_value': 0.47974544654652834, 'weights_q0.25': 0.023458148978927472, 'weights_q0.5': 0.02845278992972488, 'weights_q0.75': 0.046388686708742986, 'weights_max': 0.07653234220880864, 'weights_std': 0.017791149291582296}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 35       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 332      |\n",
      "|    total_timesteps | 44100    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -6.24    |\n",
      "|    critic_loss     | 0.000197 |\n",
      "|    ent_coef        | 3.03e-05 |\n",
      "|    ent_coef_loss   | 26.5     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.0972   |\n",
      "|    mean_reward     | 0.000374 |\n",
      "|    min_reward      | -0.184   |\n",
      "|    n_updates       | 43999    |\n",
      "|    portfolio_value | 0.471    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.17270072553927607, 'max_reward': 0.12652861005920013, 'mean_reward': 0.0004219789284768434, 'portfolio_value': 0.5316934498808233, 'weights_q0.25': 0.02786504943401195, 'weights_q0.5': 0.038329386327603104, 'weights_q0.75': 0.04192355800337967, 'weights_max': 0.06358151216273937, 'weights_std': 0.012884844346469512}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 36       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 341      |\n",
      "|    total_timesteps | 45360    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -5.49    |\n",
      "|    critic_loss     | 0.000292 |\n",
      "|    ent_coef        | 3.49e-05 |\n",
      "|    ent_coef_loss   | -19.3    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.127    |\n",
      "|    mean_reward     | 0.000415 |\n",
      "|    min_reward      | -0.173   |\n",
      "|    n_updates       | 45259    |\n",
      "|    portfolio_value | 0.523    |\n",
      "---------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'min_reward': -0.1847251193201035, 'max_reward': 0.1261287602780347, 'mean_reward': 0.0004240401513831691, 'portfolio_value': 0.5342905907427936, 'weights_q0.25': 0.0239575994655211, 'weights_q0.5': 0.03829726706397728, 'weights_q0.75': 0.04739665038164188, 'weights_max': 0.061107547998346584, 'weights_std': 0.015168599417948167}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 37       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 351      |\n",
      "|    total_timesteps | 46620    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -5.2     |\n",
      "|    critic_loss     | 0.000118 |\n",
      "|    ent_coef        | 4.19e-05 |\n",
      "|    ent_coef_loss   | -43.1    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.126    |\n",
      "|    mean_reward     | 0.000419 |\n",
      "|    min_reward      | -0.185   |\n",
      "|    n_updates       | 46519    |\n",
      "|    portfolio_value | 0.528    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.17998635435069274, 'max_reward': 0.14132002507566394, 'mean_reward': 0.00039179369614179247, 'portfolio_value': 0.4936600571386584, 'weights_q0.25': 0.018798378603668398, 'weights_q0.5': 0.027678985733717937, 'weights_q0.75': 0.05136382904661879, 'weights_max': 0.0704697900307734, 'weights_std': 0.019121752321618765}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 38       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 361      |\n",
      "|    total_timesteps | 47880    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -4.87    |\n",
      "|    critic_loss     | 0.000453 |\n",
      "|    ent_coef        | 2.74e-05 |\n",
      "|    ent_coef_loss   | -16.4    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.141    |\n",
      "|    mean_reward     | 0.000386 |\n",
      "|    min_reward      | -0.18    |\n",
      "|    n_updates       | 47779    |\n",
      "|    portfolio_value | 0.486    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.18374566867652783, 'max_reward': 0.12082281544784554, 'mean_reward': 0.0003793308890742669, 'portfolio_value': 0.4779569202335762, 'weights_q0.25': 0.020732979202368364, 'weights_q0.5': 0.03835180040318325, 'weights_q0.75': 0.04458408315649612, 'weights_max': 0.0726546125838689, 'weights_std': 0.015346211117479242}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 39       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 370      |\n",
      "|    total_timesteps | 49140    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -4.25    |\n",
      "|    critic_loss     | 0.00158  |\n",
      "|    ent_coef        | 2.83e-05 |\n",
      "|    ent_coef_loss   | -36.7    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.121    |\n",
      "|    mean_reward     | 0.000374 |\n",
      "|    min_reward      | -0.184   |\n",
      "|    n_updates       | 49039    |\n",
      "|    portfolio_value | 0.47     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.17622645092393294, 'max_reward': 0.1244833489104429, 'mean_reward': 0.0003895922025481192, 'portfolio_value': 0.4908861752106302, 'weights_q0.25': 0.016969563710429913, 'weights_q0.5': 0.033434577578248026, 'weights_q0.75': 0.0514475598378844, 'weights_max': 0.07612704531843172, 'weights_std': 0.018842084090689652}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 40       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 380      |\n",
      "|    total_timesteps | 50400    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -3.78    |\n",
      "|    critic_loss     | 8.02e-05 |\n",
      "|    ent_coef        | 2.73e-05 |\n",
      "|    ent_coef_loss   | -97.7    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.124    |\n",
      "|    mean_reward     | 0.000386 |\n",
      "|    min_reward      | -0.176   |\n",
      "|    n_updates       | 50299    |\n",
      "|    portfolio_value | 0.486    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.17162169794520699, 'max_reward': 0.09364326202079376, 'mean_reward': 0.0004078279599631075, 'portfolio_value': 0.5138632295535153, 'weights_q0.25': 0.021079920034023984, 'weights_q0.5': 0.03516039276948233, 'weights_q0.75': 0.051025663941299024, 'weights_max': 0.06767174333178055, 'weights_std': 0.01681024123846788}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 41       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 389      |\n",
      "|    total_timesteps | 51660    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -4.05    |\n",
      "|    critic_loss     | 0.000352 |\n",
      "|    ent_coef        | 2.25e-05 |\n",
      "|    ent_coef_loss   | -57.5    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.0936   |\n",
      "|    mean_reward     | 0.0004   |\n",
      "|    min_reward      | -0.172   |\n",
      "|    n_updates       | 51559    |\n",
      "|    portfolio_value | 0.503    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.17654753934362552, 'max_reward': 0.09350678824599794, 'mean_reward': 0.00034509371918907595, 'portfolio_value': 0.43481808617823603, 'weights_q0.25': 0.025004703657775004, 'weights_q0.5': 0.037392724916373854, 'weights_q0.75': 0.046114221013104785, 'weights_max': 0.06116338852532688, 'weights_std': 0.013674653620748201}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 42       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 398      |\n",
      "|    total_timesteps | 52920    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -3.28    |\n",
      "|    critic_loss     | 6.76e-05 |\n",
      "|    ent_coef        | 1.78e-05 |\n",
      "|    ent_coef_loss   | -80.3    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.0935   |\n",
      "|    mean_reward     | 0.000342 |\n",
      "|    min_reward      | -0.177   |\n",
      "|    n_updates       | 52819    |\n",
      "|    portfolio_value | 0.43     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.1748666335815415, 'max_reward': 0.1236573671086388, 'mean_reward': 0.0004936674418074196, 'portfolio_value': 0.6220209766773493, 'weights_q0.25': 0.022928533058124402, 'weights_q0.5': 0.03375092635027035, 'weights_q0.75': 0.04910991271772996, 'weights_max': 0.05984083328292338, 'weights_std': 0.014717198005306244}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 43       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 408      |\n",
      "|    total_timesteps | 54180    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -3.27    |\n",
      "|    critic_loss     | 5.51e-05 |\n",
      "|    ent_coef        | 1.65e-05 |\n",
      "|    ent_coef_loss   | -58.3    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.124    |\n",
      "|    mean_reward     | 0.000488 |\n",
      "|    min_reward      | -0.175   |\n",
      "|    n_updates       | 54079    |\n",
      "|    portfolio_value | 0.615    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.17537871359195523, 'max_reward': 0.17639276093622577, 'mean_reward': 0.0006227694616927313, 'portfolio_value': 0.7846895217328409, 'weights_q0.25': 0.019930207367470202, 'weights_q0.5': 0.03563901686455401, 'weights_q0.75': 0.05000249363736037, 'weights_max': 0.06521306759518862, 'weights_std': 0.017453093129914877}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 44       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 417      |\n",
      "|    total_timesteps | 55440    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -3.34    |\n",
      "|    critic_loss     | 0.000183 |\n",
      "|    ent_coef        | 1.94e-05 |\n",
      "|    ent_coef_loss   | -70.4    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.176    |\n",
      "|    mean_reward     | 0.000618 |\n",
      "|    min_reward      | -0.175   |\n",
      "|    n_updates       | 55339    |\n",
      "|    portfolio_value | 0.778    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.17813061223891108, 'max_reward': 0.18548935961085927, 'mean_reward': 0.0005216062840621916, 'portfolio_value': 0.6572239179183613, 'weights_q0.25': 0.017226626746869596, 'weights_q0.5': 0.037731437729888365, 'weights_q0.75': 0.049661260978802316, 'weights_max': 0.06681473031768961, 'weights_std': 0.01687817255196773}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 45       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 427      |\n",
      "|    total_timesteps | 56700    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -3.2     |\n",
      "|    critic_loss     | 0.000165 |\n",
      "|    ent_coef        | 1.63e-05 |\n",
      "|    ent_coef_loss   | 143      |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.185    |\n",
      "|    mean_reward     | 0.000516 |\n",
      "|    min_reward      | -0.178   |\n",
      "|    n_updates       | 56599    |\n",
      "|    portfolio_value | 0.649    |\n",
      "---------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'min_reward': -0.1736821187467131, 'max_reward': 0.1811198535250156, 'mean_reward': 0.000638724708357461, 'portfolio_value': 0.8047931325304012, 'weights_q0.25': 0.023594662124204108, 'weights_q0.5': 0.034944964001319925, 'weights_q0.75': 0.049388422691268356, 'weights_max': 0.06409936268122089, 'weights_std': 0.01626506933720104}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 46       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 436      |\n",
      "|    total_timesteps | 57960    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -2.51    |\n",
      "|    critic_loss     | 0.000212 |\n",
      "|    ent_coef        | 2.28e-05 |\n",
      "|    ent_coef_loss   | 42.7     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.181    |\n",
      "|    mean_reward     | 0.000634 |\n",
      "|    min_reward      | -0.174   |\n",
      "|    n_updates       | 57859    |\n",
      "|    portfolio_value | 0.798    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.17473884635974507, 'max_reward': 0.16937414810790002, 'mean_reward': 0.00047430474386697853, 'portfolio_value': 0.597623977272392, 'weights_q0.25': 0.021992111891311075, 'weights_q0.5': 0.03250450257502931, 'weights_q0.75': 0.053061467635980214, 'weights_max': 0.05797258818719327, 'weights_std': 0.01631554780821509}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 47       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 446      |\n",
      "|    total_timesteps | 59220    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -2.53    |\n",
      "|    critic_loss     | 6.89e-05 |\n",
      "|    ent_coef        | 2e-05    |\n",
      "|    ent_coef_loss   | -54.2    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.169    |\n",
      "|    mean_reward     | 0.00047  |\n",
      "|    min_reward      | -0.175   |\n",
      "|    n_updates       | 59119    |\n",
      "|    portfolio_value | 0.591    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16279691219919612, 'max_reward': 0.1663624468678606, 'mean_reward': 0.0005249328409601935, 'portfolio_value': 0.6614153796098439, 'weights_q0.25': 0.020499921754835455, 'weights_q0.5': 0.03703216299283421, 'weights_q0.75': 0.05252548852146968, 'weights_max': 0.06116361239180721, 'weights_std': 0.01696418167642589}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 48       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 455      |\n",
      "|    total_timesteps | 60480    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -2.18    |\n",
      "|    critic_loss     | 3.7e-05  |\n",
      "|    ent_coef        | 1.53e-05 |\n",
      "|    ent_coef_loss   | -97.3    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.166    |\n",
      "|    mean_reward     | 0.000521 |\n",
      "|    min_reward      | -0.163   |\n",
      "|    n_updates       | 60379    |\n",
      "|    portfolio_value | 0.655    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16405003595601383, 'max_reward': 0.168980314205468, 'mean_reward': 0.0007197616570520009, 'portfolio_value': 0.9068996878855216, 'weights_q0.25': 0.016258481342909258, 'weights_q0.5': 0.03667788914766776, 'weights_q0.75': 0.04894152964109154, 'weights_max': 0.07400004265048632, 'weights_std': 0.017958100489118046}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 49       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 465      |\n",
      "|    total_timesteps | 61740    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -2.32    |\n",
      "|    critic_loss     | 4.13e-05 |\n",
      "|    ent_coef        | 1.55e-05 |\n",
      "|    ent_coef_loss   | -81.8    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.169    |\n",
      "|    mean_reward     | 0.000713 |\n",
      "|    min_reward      | -0.164   |\n",
      "|    n_updates       | 61639    |\n",
      "|    portfolio_value | 0.897    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16258078003816817, 'max_reward': 0.14203147641974148, 'mean_reward': 0.0006754319577077379, 'portfolio_value': 0.851044266711749, 'weights_q0.25': 0.0202237309414147, 'weights_q0.5': 0.040713609654557836, 'weights_q0.75': 0.049147992751410706, 'weights_max': 0.06339165302763013, 'weights_std': 0.017275141768025533}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 50       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 475      |\n",
      "|    total_timesteps | 63000    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -2.02    |\n",
      "|    critic_loss     | 0.000194 |\n",
      "|    ent_coef        | 1.73e-05 |\n",
      "|    ent_coef_loss   | -11      |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.142    |\n",
      "|    mean_reward     | 0.000672 |\n",
      "|    min_reward      | -0.163   |\n",
      "|    n_updates       | 62899    |\n",
      "|    portfolio_value | 0.846    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.1638754717166644, 'max_reward': 0.1822102846069124, 'mean_reward': 0.0007816930488986455, 'portfolio_value': 0.9849332416122955, 'weights_q0.25': 0.01686386957182767, 'weights_q0.5': 0.0309758905954702, 'weights_q0.75': 0.04835658034818811, 'weights_max': 0.07763417076020881, 'weights_std': 0.022000454554837127}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 51       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 484      |\n",
      "|    total_timesteps | 64260    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -2       |\n",
      "|    critic_loss     | 5.58e-05 |\n",
      "|    ent_coef        | 1.34e-05 |\n",
      "|    ent_coef_loss   | 117      |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.182    |\n",
      "|    mean_reward     | 0.000776 |\n",
      "|    min_reward      | -0.164   |\n",
      "|    n_updates       | 64159    |\n",
      "|    portfolio_value | 0.977    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16521114240341478, 'max_reward': 0.19237397588239974, 'mean_reward': 0.0007862131682197302, 'portfolio_value': 0.9906285919568593, 'weights_q0.25': 0.019248460492127296, 'weights_q0.5': 0.03467685665007186, 'weights_q0.75': 0.053167686510994835, 'weights_max': 0.06306844105420983, 'weights_std': 0.01810039685820458}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 52       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 493      |\n",
      "|    total_timesteps | 65520    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -1.87    |\n",
      "|    critic_loss     | 4.94e-05 |\n",
      "|    ent_coef        | 1.2e-05  |\n",
      "|    ent_coef_loss   | -30.5    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.192    |\n",
      "|    mean_reward     | 0.000784 |\n",
      "|    min_reward      | -0.165   |\n",
      "|    n_updates       | 65419    |\n",
      "|    portfolio_value | 0.987    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.1649482700497305, 'max_reward': 0.18882827301391314, 'mean_reward': 0.0007877378524530623, 'portfolio_value': 0.9925496940908587, 'weights_q0.25': 0.01715668432914042, 'weights_q0.5': 0.025086194671788464, 'weights_q0.75': 0.05473012356478289, 'weights_max': 0.07628494889084123, 'weights_std': 0.021941024847648254}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 53       |\n",
      "|    fps             | 132      |\n",
      "|    time_elapsed    | 502      |\n",
      "|    total_timesteps | 66780    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -1.52    |\n",
      "|    critic_loss     | 4.17e-05 |\n",
      "|    ent_coef        | 1.15e-05 |\n",
      "|    ent_coef_loss   | -45.8    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.189    |\n",
      "|    mean_reward     | 0.000785 |\n",
      "|    min_reward      | -0.165   |\n",
      "|    n_updates       | 66679    |\n",
      "|    portfolio_value | 0.989    |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16177867329792464, 'max_reward': 0.18514912833614627, 'mean_reward': 0.0008312899354590206, 'portfolio_value': 1.0474253186783666, 'weights_q0.25': 0.015056626022350693, 'weights_q0.5': 0.029110514733050367, 'weights_q0.75': 0.059380495553566524, 'weights_max': 0.06762562224572112, 'weights_std': 0.020956759245800205}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 54       |\n",
      "|    fps             | 133      |\n",
      "|    time_elapsed    | 511      |\n",
      "|    total_timesteps | 68040    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -1.67    |\n",
      "|    critic_loss     | 4.81e-05 |\n",
      "|    ent_coef        | 1e-05    |\n",
      "|    ent_coef_loss   | -85.3    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.185    |\n",
      "|    mean_reward     | 0.000828 |\n",
      "|    min_reward      | -0.162   |\n",
      "|    n_updates       | 67939    |\n",
      "|    portfolio_value | 1.04     |\n",
      "---------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'min_reward': -0.16201415375429612, 'max_reward': 0.17985844642483345, 'mean_reward': 0.0009347836474341463, 'portfolio_value': 1.177827395767024, 'weights_q0.25': 0.020355520718922278, 'weights_q0.5': 0.03408151922220193, 'weights_q0.75': 0.04620924343830764, 'weights_max': 0.06663302462016972, 'weights_std': 0.017690262268660144}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 55       |\n",
      "|    fps             | 133      |\n",
      "|    time_elapsed    | 520      |\n",
      "|    total_timesteps | 69300    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -1.47    |\n",
      "|    critic_loss     | 3.33e-05 |\n",
      "|    ent_coef        | 1.48e-05 |\n",
      "|    ent_coef_loss   | -12.1    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.18     |\n",
      "|    mean_reward     | 0.000929 |\n",
      "|    min_reward      | -0.162   |\n",
      "|    n_updates       | 69199    |\n",
      "|    portfolio_value | 1.17     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16007123496801204, 'max_reward': 0.19087680906851573, 'mean_reward': 0.0009841736524521067, 'portfolio_value': 1.2400588020896552, 'weights_q0.25': 0.016173422895104206, 'weights_q0.5': 0.03813494955769692, 'weights_q0.75': 0.05185380983235645, 'weights_max': 0.0675389583054076, 'weights_std': 0.018307745551979115}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 56       |\n",
      "|    fps             | 133      |\n",
      "|    time_elapsed    | 529      |\n",
      "|    total_timesteps | 70560    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -1.4     |\n",
      "|    critic_loss     | 2.41e-05 |\n",
      "|    ent_coef        | 1.17e-05 |\n",
      "|    ent_coef_loss   | -68.3    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.191    |\n",
      "|    mean_reward     | 0.000981 |\n",
      "|    min_reward      | -0.16    |\n",
      "|    n_updates       | 70459    |\n",
      "|    portfolio_value | 1.23     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16346698383536015, 'max_reward': 0.19224907017672266, 'mean_reward': 0.000959065495409017, 'portfolio_value': 1.2084225242153617, 'weights_q0.25': 0.016566169460972238, 'weights_q0.5': 0.03478361913368526, 'weights_q0.75': 0.05448762090727709, 'weights_max': 0.06341813905810444, 'weights_std': 0.018463030208985286}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 57       |\n",
      "|    fps             | 133      |\n",
      "|    time_elapsed    | 538      |\n",
      "|    total_timesteps | 71820    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -1.39    |\n",
      "|    critic_loss     | 1.96e-05 |\n",
      "|    ent_coef        | 9.52e-06 |\n",
      "|    ent_coef_loss   | 80.3     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.192    |\n",
      "|    mean_reward     | 0.000954 |\n",
      "|    min_reward      | -0.163   |\n",
      "|    n_updates       | 71719    |\n",
      "|    portfolio_value | 1.2      |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16201865196187049, 'max_reward': 0.1929381372906105, 'mean_reward': 0.0009825331829168473, 'portfolio_value': 1.2379918104752274, 'weights_q0.25': 0.013579788309756488, 'weights_q0.5': 0.029662314319030172, 'weights_q0.75': 0.05704514783109276, 'weights_max': 0.06860290534258569, 'weights_std': 0.021719205364096855}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 58       |\n",
      "|    fps             | 133      |\n",
      "|    time_elapsed    | 547      |\n",
      "|    total_timesteps | 73080    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -1.14    |\n",
      "|    critic_loss     | 1.92e-05 |\n",
      "|    ent_coef        | 9.25e-06 |\n",
      "|    ent_coef_loss   | -37      |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.193    |\n",
      "|    mean_reward     | 0.000978 |\n",
      "|    min_reward      | -0.162   |\n",
      "|    n_updates       | 72979    |\n",
      "|    portfolio_value | 1.23     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16091126988185434, 'max_reward': 0.19240122286238936, 'mean_reward': 0.0011142747886927996, 'portfolio_value': 1.4039862337529274, 'weights_q0.25': 0.01414787855217054, 'weights_q0.5': 0.04071872673912109, 'weights_q0.75': 0.05564143877067772, 'weights_max': 0.06450710713853296, 'weights_std': 0.02016122028759122}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 59       |\n",
      "|    fps             | 133      |\n",
      "|    time_elapsed    | 555      |\n",
      "|    total_timesteps | 74340    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -1.08    |\n",
      "|    critic_loss     | 6.66e-05 |\n",
      "|    ent_coef        | 8.42e-06 |\n",
      "|    ent_coef_loss   | 36.3     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.192    |\n",
      "|    mean_reward     | 0.00111  |\n",
      "|    min_reward      | -0.161   |\n",
      "|    n_updates       | 74239    |\n",
      "|    portfolio_value | 1.4      |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16305518030360186, 'max_reward': 0.19168926015171917, 'mean_reward': 0.0011268467125665077, 'portfolio_value': 1.4198268578338011, 'weights_q0.25': 0.017848435945200226, 'weights_q0.5': 0.035559292162027495, 'weights_q0.75': 0.05080160754416924, 'weights_max': 0.067841347491841, 'weights_std': 0.01860010593644649}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 60       |\n",
      "|    fps             | 133      |\n",
      "|    time_elapsed    | 564      |\n",
      "|    total_timesteps | 75600    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -1.04    |\n",
      "|    critic_loss     | 2.2e-05  |\n",
      "|    ent_coef        | 7.92e-06 |\n",
      "|    ent_coef_loss   | -37.6    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.192    |\n",
      "|    mean_reward     | 0.00112  |\n",
      "|    min_reward      | -0.163   |\n",
      "|    n_updates       | 75499    |\n",
      "|    portfolio_value | 1.41     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16304612312277894, 'max_reward': 0.1937619053606225, 'mean_reward': 0.0011712833936555562, 'portfolio_value': 1.4758170760060016, 'weights_q0.25': 0.01870575705369908, 'weights_q0.5': 0.03640173839339378, 'weights_q0.75': 0.05289994683267066, 'weights_max': 0.059919899149076125, 'weights_std': 0.017905055530830074}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 61       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 573      |\n",
      "|    total_timesteps | 76860    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -1.03    |\n",
      "|    critic_loss     | 2.19e-05 |\n",
      "|    ent_coef        | 7.59e-06 |\n",
      "|    ent_coef_loss   | -48.6    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.194    |\n",
      "|    mean_reward     | 0.00117  |\n",
      "|    min_reward      | -0.163   |\n",
      "|    n_updates       | 76759    |\n",
      "|    portfolio_value | 1.47     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16107691275500757, 'max_reward': 0.19289887552496138, 'mean_reward': 0.0011647348534574748, 'portfolio_value': 1.4675659153564182, 'weights_q0.25': 0.012976560048905045, 'weights_q0.5': 0.04079666486333214, 'weights_q0.75': 0.05093688338799291, 'weights_max': 0.05903183980163894, 'weights_std': 0.01842719057339695}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 62       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 582      |\n",
      "|    total_timesteps | 78120    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.929   |\n",
      "|    critic_loss     | 5.64e-05 |\n",
      "|    ent_coef        | 7.95e-06 |\n",
      "|    ent_coef_loss   | 50.7     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.193    |\n",
      "|    mean_reward     | 0.00116  |\n",
      "|    min_reward      | -0.161   |\n",
      "|    n_updates       | 78019    |\n",
      "|    portfolio_value | 1.46     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16178673954750108, 'max_reward': 0.18852407872448648, 'mean_reward': 0.0011995381637513736, 'portfolio_value': 1.5114180863267312, 'weights_q0.25': 0.014806658869718934, 'weights_q0.5': 0.031003979683069338, 'weights_q0.75': 0.05639628999246722, 'weights_max': 0.06284096780754561, 'weights_std': 0.020385394889724656}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 63       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 591      |\n",
      "|    total_timesteps | 79380    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.844   |\n",
      "|    critic_loss     | 3.27e-05 |\n",
      "|    ent_coef        | 7.91e-06 |\n",
      "|    ent_coef_loss   | -22.4    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.189    |\n",
      "|    mean_reward     | 0.0012   |\n",
      "|    min_reward      | -0.162   |\n",
      "|    n_updates       | 79279    |\n",
      "|    portfolio_value | 1.51     |\n",
      "---------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'min_reward': -0.16129926027692598, 'max_reward': 0.19492403759206606, 'mean_reward': 0.0011769698812176164, 'portfolio_value': 1.4829820503341984, 'weights_q0.25': 0.016827653631637463, 'weights_q0.5': 0.03194609494292505, 'weights_q0.75': 0.05794576045036908, 'weights_max': 0.06333424966795773, 'weights_std': 0.02092031922856697}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 64       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 600      |\n",
      "|    total_timesteps | 80640    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.755   |\n",
      "|    critic_loss     | 8.71e-05 |\n",
      "|    ent_coef        | 7.05e-06 |\n",
      "|    ent_coef_loss   | 94.7     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.195    |\n",
      "|    mean_reward     | 0.00117  |\n",
      "|    min_reward      | -0.161   |\n",
      "|    n_updates       | 80539    |\n",
      "|    portfolio_value | 1.48     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16740609591364725, 'max_reward': 0.1905473953824029, 'mean_reward': 0.0012599444663152467, 'portfolio_value': 1.5875300275572102, 'weights_q0.25': 0.019344160301865723, 'weights_q0.5': 0.039171823510892385, 'weights_q0.75': 0.051652860046207526, 'weights_max': 0.05951654551348847, 'weights_std': 0.01725828802223425}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 65       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 609      |\n",
      "|    total_timesteps | 81900    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.765   |\n",
      "|    critic_loss     | 7.55e-06 |\n",
      "|    ent_coef        | 6.99e-06 |\n",
      "|    ent_coef_loss   | -1.89    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.191    |\n",
      "|    mean_reward     | 0.00126  |\n",
      "|    min_reward      | -0.167   |\n",
      "|    n_updates       | 81799    |\n",
      "|    portfolio_value | 1.58     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16246604363029324, 'max_reward': 0.1867605225664149, 'mean_reward': 0.0013297792308591262, 'portfolio_value': 1.6755218308824993, 'weights_q0.25': 0.01703989787543884, 'weights_q0.5': 0.03819735952345116, 'weights_q0.75': 0.053735499504469056, 'weights_max': 0.06051189996993948, 'weights_std': 0.018514495143135534}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 66       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 617      |\n",
      "|    total_timesteps | 83160    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.728   |\n",
      "|    critic_loss     | 2.71e-05 |\n",
      "|    ent_coef        | 7.69e-06 |\n",
      "|    ent_coef_loss   | -47.1    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.187    |\n",
      "|    mean_reward     | 0.00133  |\n",
      "|    min_reward      | -0.162   |\n",
      "|    n_updates       | 83059    |\n",
      "|    portfolio_value | 1.67     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16546092782321326, 'max_reward': 0.18668490566759519, 'mean_reward': 0.0013545889183940584, 'portfolio_value': 1.7067820371765137, 'weights_q0.25': 0.013987309887749771, 'weights_q0.5': 0.04030745952758626, 'weights_q0.75': 0.0570922165670999, 'weights_max': 0.06215085816961929, 'weights_std': 0.02064899194743622}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 67       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 626      |\n",
      "|    total_timesteps | 84420    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.694   |\n",
      "|    critic_loss     | 1.05e-05 |\n",
      "|    ent_coef        | 7.79e-06 |\n",
      "|    ent_coef_loss   | 14.4     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.187    |\n",
      "|    mean_reward     | 0.00135  |\n",
      "|    min_reward      | -0.165   |\n",
      "|    n_updates       | 84319    |\n",
      "|    portfolio_value | 1.7      |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16303771230380654, 'max_reward': 0.19516517915082549, 'mean_reward': 0.0014399989117221182, 'portfolio_value': 1.814398628769867, 'weights_q0.25': 0.011968199995175663, 'weights_q0.5': 0.04295313104760212, 'weights_q0.75': 0.05862087991150167, 'weights_max': 0.0689723211161705, 'weights_std': 0.02255884149299683}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 68       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 635      |\n",
      "|    total_timesteps | 85680    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.672   |\n",
      "|    critic_loss     | 7.54e-06 |\n",
      "|    ent_coef        | 6.65e-06 |\n",
      "|    ent_coef_loss   | -34.6    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.195    |\n",
      "|    mean_reward     | 0.00144  |\n",
      "|    min_reward      | -0.163   |\n",
      "|    n_updates       | 85579    |\n",
      "|    portfolio_value | 1.81     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.17128911081530618, 'max_reward': 0.1900336732766067, 'mean_reward': 0.0013986876494033764, 'portfolio_value': 1.7623464382482563, 'weights_q0.25': 0.011497854433711771, 'weights_q0.5': 0.039586709541429455, 'weights_q0.75': 0.05779077541152136, 'weights_max': 0.06180438271178598, 'weights_std': 0.02161135555809128}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 69       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 644      |\n",
      "|    total_timesteps | 86940    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.576   |\n",
      "|    critic_loss     | 1.5e-05  |\n",
      "|    ent_coef        | 6.86e-06 |\n",
      "|    ent_coef_loss   | 26.8     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.19     |\n",
      "|    mean_reward     | 0.0014   |\n",
      "|    min_reward      | -0.171   |\n",
      "|    n_updates       | 86839    |\n",
      "|    portfolio_value | 1.76     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16593598833076934, 'max_reward': 0.19222948563403844, 'mean_reward': 0.0014869776855972773, 'portfolio_value': 1.8735918838525707, 'weights_q0.25': 0.011126004130136053, 'weights_q0.5': 0.037698591940155, 'weights_q0.75': 0.05930015417751409, 'weights_max': 0.06535187150258166, 'weights_std': 0.02300974475663888}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 70       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 654      |\n",
      "|    total_timesteps | 88200    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.599   |\n",
      "|    critic_loss     | 1.1e-05  |\n",
      "|    ent_coef        | 6.52e-06 |\n",
      "|    ent_coef_loss   | -3.25    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.192    |\n",
      "|    mean_reward     | 0.00149  |\n",
      "|    min_reward      | -0.166   |\n",
      "|    n_updates       | 88099    |\n",
      "|    portfolio_value | 1.87     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16312855956584174, 'max_reward': 0.18878315783331107, 'mean_reward': 0.0015729248699632003, 'portfolio_value': 1.9818853361536335, 'weights_q0.25': 0.01239241631013812, 'weights_q0.5': 0.021520781311990873, 'weights_q0.75': 0.05879995887180855, 'weights_max': 0.07123182606607772, 'weights_std': 0.024068905396391418}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 71       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 663      |\n",
      "|    total_timesteps | 89460    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.527   |\n",
      "|    critic_loss     | 9.85e-06 |\n",
      "|    ent_coef        | 6.53e-06 |\n",
      "|    ent_coef_loss   | -34.9    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.189    |\n",
      "|    mean_reward     | 0.00157  |\n",
      "|    min_reward      | -0.163   |\n",
      "|    n_updates       | 89359    |\n",
      "|    portfolio_value | 1.98     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15825878986202008, 'max_reward': 0.18716766718479322, 'mean_reward': 0.0016147443974609478, 'portfolio_value': 2.0345779408007933, 'weights_q0.25': 0.01222254752534084, 'weights_q0.5': 0.022083211870931554, 'weights_q0.75': 0.06629947256634028, 'weights_max': 0.07107017649551739, 'weights_std': 0.02518525416561659}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 72       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 673      |\n",
      "|    total_timesteps | 90720    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.522   |\n",
      "|    critic_loss     | 4.08e-06 |\n",
      "|    ent_coef        | 6.58e-06 |\n",
      "|    ent_coef_loss   | 19.7     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.187    |\n",
      "|    mean_reward     | 0.00161  |\n",
      "|    min_reward      | -0.158   |\n",
      "|    n_updates       | 90619    |\n",
      "|    portfolio_value | 2.03     |\n",
      "---------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'min_reward': -0.15903538141239526, 'max_reward': 0.19465908264178505, 'mean_reward': 0.0017017501730014304, 'portfolio_value': 2.1442052179817996, 'weights_q0.25': 0.012348574260061033, 'weights_q0.5': 0.03082766224543623, 'weights_q0.75': 0.06455330883617436, 'weights_max': 0.07084642086029257, 'weights_std': 0.02338975256960003}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 73       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 682      |\n",
      "|    total_timesteps | 91980    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.481   |\n",
      "|    critic_loss     | 4.53e-06 |\n",
      "|    ent_coef        | 7.07e-06 |\n",
      "|    ent_coef_loss   | 25.2     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.195    |\n",
      "|    mean_reward     | 0.0017   |\n",
      "|    min_reward      | -0.159   |\n",
      "|    n_updates       | 91879    |\n",
      "|    portfolio_value | 2.14     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15864203055920484, 'max_reward': 0.16890878800647854, 'mean_reward': 0.0016515777817070062, 'portfolio_value': 2.080988004950826, 'weights_q0.25': 0.012147181624436704, 'weights_q0.5': 0.025991768785133183, 'weights_q0.75': 0.06428590183844271, 'weights_max': 0.07043633697072367, 'weights_std': 0.024436849830651284}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 74       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 692      |\n",
      "|    total_timesteps | 93240    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.453   |\n",
      "|    critic_loss     | 9.11e-06 |\n",
      "|    ent_coef        | 6.79e-06 |\n",
      "|    ent_coef_loss   | -30      |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.169    |\n",
      "|    mean_reward     | 0.00165  |\n",
      "|    min_reward      | -0.159   |\n",
      "|    n_updates       | 93139    |\n",
      "|    portfolio_value | 2.08     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15806059191625532, 'max_reward': 0.1950068448241733, 'mean_reward': 0.001736187706899526, 'portfolio_value': 2.1875965106934037, 'weights_q0.25': 0.0108509608110392, 'weights_q0.5': 0.03508071309265752, 'weights_q0.75': 0.06188709782501811, 'weights_max': 0.06688130555228464, 'weights_std': 0.024448187654191645}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 75       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 701      |\n",
      "|    total_timesteps | 94500    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.397   |\n",
      "|    critic_loss     | 8.76e-06 |\n",
      "|    ent_coef        | 6.68e-06 |\n",
      "|    ent_coef_loss   | 33       |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.195    |\n",
      "|    mean_reward     | 0.00173  |\n",
      "|    min_reward      | -0.158   |\n",
      "|    n_updates       | 94399    |\n",
      "|    portfolio_value | 2.18     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15910832494808408, 'max_reward': 0.19491022747180434, 'mean_reward': 0.0018492699194138382, 'portfolio_value': 2.3300800984614365, 'weights_q0.25': 0.011192806640923899, 'weights_q0.5': 0.027081461222620458, 'weights_q0.75': 0.06265615872918723, 'weights_max': 0.07078716985979534, 'weights_std': 0.025140793057632456}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 76       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 710      |\n",
      "|    total_timesteps | 95760    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.41    |\n",
      "|    critic_loss     | 6.6e-05  |\n",
      "|    ent_coef        | 7.05e-06 |\n",
      "|    ent_coef_loss   | -12.6    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.195    |\n",
      "|    mean_reward     | 0.00185  |\n",
      "|    min_reward      | -0.159   |\n",
      "|    n_updates       | 95659    |\n",
      "|    portfolio_value | 2.33     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15439884784946714, 'max_reward': 0.19195286685039256, 'mean_reward': 0.001981843075429307, 'portfolio_value': 2.497122275040929, 'weights_q0.25': 0.010677540886778043, 'weights_q0.5': 0.0251862389750921, 'weights_q0.75': 0.06492844044508066, 'weights_max': 0.06862649487270263, 'weights_std': 0.025761596653321336}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 77       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 719      |\n",
      "|    total_timesteps | 97020    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.379   |\n",
      "|    critic_loss     | 2.28e-05 |\n",
      "|    ent_coef        | 7.54e-06 |\n",
      "|    ent_coef_loss   | 13       |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.192    |\n",
      "|    mean_reward     | 0.00198  |\n",
      "|    min_reward      | -0.154   |\n",
      "|    n_updates       | 96919    |\n",
      "|    portfolio_value | 2.49     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15709048897707342, 'max_reward': 0.2011290257709375, 'mean_reward': 0.002007882128931747, 'portfolio_value': 2.5299314824540007, 'weights_q0.25': 0.008758958792830136, 'weights_q0.5': 0.043069291412030475, 'weights_q0.75': 0.06062919900677148, 'weights_max': 0.06421001804557754, 'weights_std': 0.023849780685428625}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 78       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 728      |\n",
      "|    total_timesteps | 98280    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.361   |\n",
      "|    critic_loss     | 6.62e-05 |\n",
      "|    ent_coef        | 7.57e-06 |\n",
      "|    ent_coef_loss   | -25.8    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.201    |\n",
      "|    mean_reward     | 0.00201  |\n",
      "|    min_reward      | -0.157   |\n",
      "|    n_updates       | 98179    |\n",
      "|    portfolio_value | 2.53     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15720874752694583, 'max_reward': 0.20408561246346918, 'mean_reward': 0.00204673569291625, 'portfolio_value': 2.5788869730744755, 'weights_q0.25': 0.009478742031418972, 'weights_q0.5': 0.03745270974503508, 'weights_q0.75': 0.060518314586484524, 'weights_max': 0.06599105351048076, 'weights_std': 0.025092995727708196}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 79       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 738      |\n",
      "|    total_timesteps | 99540    |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.355   |\n",
      "|    critic_loss     | 6.3e-05  |\n",
      "|    ent_coef        | 8.16e-06 |\n",
      "|    ent_coef_loss   | -4.09    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.204    |\n",
      "|    mean_reward     | 0.00205  |\n",
      "|    min_reward      | -0.157   |\n",
      "|    n_updates       | 99439    |\n",
      "|    portfolio_value | 2.58     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15710481027282872, 'max_reward': 0.2083058680291145, 'mean_reward': 0.002093054515895661, 'portfolio_value': 2.6372486900285312, 'weights_q0.25': 0.009240333232984786, 'weights_q0.5': 0.0384720518758096, 'weights_q0.75': 0.061975739121358814, 'weights_max': 0.0630407724594517, 'weights_std': 0.02483116093722303}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 80       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 747      |\n",
      "|    total_timesteps | 100800   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.333   |\n",
      "|    critic_loss     | 1.23e-05 |\n",
      "|    ent_coef        | 8.11e-06 |\n",
      "|    ent_coef_loss   | -20.7    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.208    |\n",
      "|    mean_reward     | 0.00209  |\n",
      "|    min_reward      | -0.157   |\n",
      "|    n_updates       | 100699   |\n",
      "|    portfolio_value | 2.63     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.1560465107274943, 'max_reward': 0.20664246190726604, 'mean_reward': 0.0021564001796717798, 'portfolio_value': 2.7170642263864426, 'weights_q0.25': 0.009423872620405088, 'weights_q0.5': 0.03982268384853385, 'weights_q0.75': 0.06298952548286726, 'weights_max': 0.06593892236134713, 'weights_std': 0.024734379911316384}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 81       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 756      |\n",
      "|    total_timesteps | 102060   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.318   |\n",
      "|    critic_loss     | 6.53e-05 |\n",
      "|    ent_coef        | 7.97e-06 |\n",
      "|    ent_coef_loss   | 10.2     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.207    |\n",
      "|    mean_reward     | 0.00216  |\n",
      "|    min_reward      | -0.156   |\n",
      "|    n_updates       | 101959   |\n",
      "|    portfolio_value | 2.72     |\n",
      "---------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'min_reward': -0.16329311547622416, 'max_reward': 0.20892302497041898, 'mean_reward': 0.0020550741160210444, 'portfolio_value': 2.5893933861865137, 'weights_q0.25': 0.00940066271644853, 'weights_q0.5': 0.0375506205641063, 'weights_q0.75': 0.060620683507172056, 'weights_max': 0.06456678381647428, 'weights_std': 0.02386728075981224}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 82       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 765      |\n",
      "|    total_timesteps | 103320   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.305   |\n",
      "|    critic_loss     | 8.48e-06 |\n",
      "|    ent_coef        | 8.04e-06 |\n",
      "|    ent_coef_loss   | -8.34    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.209    |\n",
      "|    mean_reward     | 0.00205  |\n",
      "|    min_reward      | -0.163   |\n",
      "|    n_updates       | 103219   |\n",
      "|    portfolio_value | 2.59     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.1526045363690866, 'max_reward': 0.2064672193914071, 'mean_reward': 0.002202116597485133, 'portfolio_value': 2.7746669128312687, 'weights_q0.25': 0.008825073028181925, 'weights_q0.5': 0.03643506256330678, 'weights_q0.75': 0.060436681838604234, 'weights_max': 0.0639868985920774, 'weights_std': 0.02483301703229652}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 83       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 775      |\n",
      "|    total_timesteps | 104580   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.291   |\n",
      "|    critic_loss     | 2.66e-05 |\n",
      "|    ent_coef        | 7.91e-06 |\n",
      "|    ent_coef_loss   | 0.0695   |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.206    |\n",
      "|    mean_reward     | 0.0022   |\n",
      "|    min_reward      | -0.153   |\n",
      "|    n_updates       | 104479   |\n",
      "|    portfolio_value | 2.77     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15147760398306748, 'max_reward': 0.20722668872332833, 'mean_reward': 0.0022645518746821926, 'portfolio_value': 2.8533353620995654, 'weights_q0.25': 0.009939741236248703, 'weights_q0.5': 0.0321563635184267, 'weights_q0.75': 0.06250518199748417, 'weights_max': 0.06747484093214313, 'weights_std': 0.024432329089651192}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 84       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 784      |\n",
      "|    total_timesteps | 105840   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.271   |\n",
      "|    critic_loss     | 2.55e-05 |\n",
      "|    ent_coef        | 8.13e-06 |\n",
      "|    ent_coef_loss   | -2.74    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.207    |\n",
      "|    mean_reward     | 0.00226  |\n",
      "|    min_reward      | -0.151   |\n",
      "|    n_updates       | 105739   |\n",
      "|    portfolio_value | 2.85     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15172424356856973, 'max_reward': 0.1990023805155186, 'mean_reward': 0.0023807521829696597, 'portfolio_value': 2.9997477505417716, 'weights_q0.25': 0.009484618991811385, 'weights_q0.5': 0.03537662792511198, 'weights_q0.75': 0.061424731791501645, 'weights_max': 0.06494909970900152, 'weights_std': 0.02466191984466706}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 85       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 793      |\n",
      "|    total_timesteps | 107100   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.295   |\n",
      "|    critic_loss     | 5.67e-05 |\n",
      "|    ent_coef        | 8.22e-06 |\n",
      "|    ent_coef_loss   | 5.48     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.199    |\n",
      "|    mean_reward     | 0.00238  |\n",
      "|    min_reward      | -0.152   |\n",
      "|    n_updates       | 106999   |\n",
      "|    portfolio_value | 3        |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15183591826175033, 'max_reward': 0.20602213415587833, 'mean_reward': 0.0023654850069384043, 'portfolio_value': 2.9805111087423883, 'weights_q0.25': 0.010358750294890046, 'weights_q0.5': 0.026928176208599837, 'weights_q0.75': 0.06233832206343043, 'weights_max': 0.06622753028504644, 'weights_std': 0.02459662675291676}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 86       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 803      |\n",
      "|    total_timesteps | 108360   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.283   |\n",
      "|    critic_loss     | 7.54e-05 |\n",
      "|    ent_coef        | 8.41e-06 |\n",
      "|    ent_coef_loss   | -15      |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.206    |\n",
      "|    mean_reward     | 0.00237  |\n",
      "|    min_reward      | -0.152   |\n",
      "|    n_updates       | 108259   |\n",
      "|    portfolio_value | 2.98     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15128699928339004, 'max_reward': 0.2086762957753991, 'mean_reward': 0.0024338548034914826, 'portfolio_value': 3.0666570523992704, 'weights_q0.25': 0.010463199166682287, 'weights_q0.5': 0.030270694812552228, 'weights_q0.75': 0.06245314849780306, 'weights_max': 0.06612890365093571, 'weights_std': 0.02520119272630978}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 87       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 812      |\n",
      "|    total_timesteps | 109620   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.252   |\n",
      "|    critic_loss     | 3.7e-06  |\n",
      "|    ent_coef        | 8.39e-06 |\n",
      "|    ent_coef_loss   | -32.5    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.209    |\n",
      "|    mean_reward     | 0.00243  |\n",
      "|    min_reward      | -0.151   |\n",
      "|    n_updates       | 109519   |\n",
      "|    portfolio_value | 3.06     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15273003770395716, 'max_reward': 0.18845594356673606, 'mean_reward': 0.0024316460211801427, 'portfolio_value': 3.0638739866869784, 'weights_q0.25': 0.009993697295919727, 'weights_q0.5': 0.026620465905981924, 'weights_q0.75': 0.06622465067207611, 'weights_max': 0.07085985039506527, 'weights_std': 0.026447516378163928}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 88       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 821      |\n",
      "|    total_timesteps | 110880   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.261   |\n",
      "|    critic_loss     | 4.03e-05 |\n",
      "|    ent_coef        | 7.95e-06 |\n",
      "|    ent_coef_loss   | -13.1    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.188    |\n",
      "|    mean_reward     | 0.00243  |\n",
      "|    min_reward      | -0.153   |\n",
      "|    n_updates       | 110779   |\n",
      "|    portfolio_value | 3.06     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15146062099813665, 'max_reward': 0.2051902428374682, 'mean_reward': 0.0024641086840281124, 'portfolio_value': 3.104776941875422, 'weights_q0.25': 0.010216404335946668, 'weights_q0.5': 0.017221202237327038, 'weights_q0.75': 0.06335259403792291, 'weights_max': 0.06935305390887281, 'weights_std': 0.026438788851826166}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 89       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 831      |\n",
      "|    total_timesteps | 112140   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.254   |\n",
      "|    critic_loss     | 6.76e-06 |\n",
      "|    ent_coef        | 9.06e-06 |\n",
      "|    ent_coef_loss   | -7.18    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.205    |\n",
      "|    mean_reward     | 0.00247  |\n",
      "|    min_reward      | -0.151   |\n",
      "|    n_updates       | 112039   |\n",
      "|    portfolio_value | 3.1      |\n",
      "---------------------------------\n",
      "{'min_reward': -0.14949396207120658, 'max_reward': 0.2047663649429599, 'mean_reward': 0.002596245243737928, 'portfolio_value': 3.271269007109793, 'weights_q0.25': 0.010309901140246238, 'weights_q0.5': 0.02374572232636188, 'weights_q0.75': 0.06264416887627082, 'weights_max': 0.07257784559579274, 'weights_std': 0.02624551132713372}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 90       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 840      |\n",
      "|    total_timesteps | 113400   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.231   |\n",
      "|    critic_loss     | 5.55e-05 |\n",
      "|    ent_coef        | 9.02e-06 |\n",
      "|    ent_coef_loss   | -16.1    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.205    |\n",
      "|    mean_reward     | 0.0026   |\n",
      "|    min_reward      | -0.149   |\n",
      "|    n_updates       | 113299   |\n",
      "|    portfolio_value | 3.27     |\n",
      "---------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'min_reward': -0.148852155155832, 'max_reward': 0.20740296681401243, 'mean_reward': 0.002624007690291726, 'portfolio_value': 3.306249689767573, 'weights_q0.25': 0.010215166089351965, 'weights_q0.5': 0.02402263192654776, 'weights_q0.75': 0.06394093514334284, 'weights_max': 0.06998422799577392, 'weights_std': 0.026101145032296155}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 91       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 850      |\n",
      "|    total_timesteps | 114660   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.246   |\n",
      "|    critic_loss     | 4.81e-05 |\n",
      "|    ent_coef        | 9.08e-06 |\n",
      "|    ent_coef_loss   | -13.8    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.207    |\n",
      "|    mean_reward     | 0.00263  |\n",
      "|    min_reward      | -0.149   |\n",
      "|    n_updates       | 114559   |\n",
      "|    portfolio_value | 3.31     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15640851593448632, 'max_reward': 0.20632783449515285, 'mean_reward': 0.0025640657070448002, 'portfolio_value': 3.2307227908764524, 'weights_q0.25': 0.010176084176505154, 'weights_q0.5': 0.02607275990880077, 'weights_q0.75': 0.06258180720476406, 'weights_max': 0.07132487319467902, 'weights_std': 0.0261032188624508}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 92       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 859      |\n",
      "|    total_timesteps | 115920   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.229   |\n",
      "|    critic_loss     | 1.87e-05 |\n",
      "|    ent_coef        | 8.72e-06 |\n",
      "|    ent_coef_loss   | 12.4     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.206    |\n",
      "|    mean_reward     | 0.00257  |\n",
      "|    min_reward      | -0.156   |\n",
      "|    n_updates       | 115819   |\n",
      "|    portfolio_value | 3.23     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.14947937003702405, 'max_reward': 0.20769969934166554, 'mean_reward': 0.0026696310416519757, 'portfolio_value': 3.3637351124814936, 'weights_q0.25': 0.012078966134955906, 'weights_q0.5': 0.01921265176225769, 'weights_q0.75': 0.0621088446400357, 'weights_max': 0.07894913805158475, 'weights_std': 0.026783766299890377}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 93       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 868      |\n",
      "|    total_timesteps | 117180   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.231   |\n",
      "|    critic_loss     | 3.23e-05 |\n",
      "|    ent_coef        | 8.87e-06 |\n",
      "|    ent_coef_loss   | 20.2     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.208    |\n",
      "|    mean_reward     | 0.00267  |\n",
      "|    min_reward      | -0.149   |\n",
      "|    n_updates       | 117079   |\n",
      "|    portfolio_value | 3.36     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.1511640458329315, 'max_reward': 0.20711698568734144, 'mean_reward': 0.0025338648875762363, 'portfolio_value': 3.1926697583460553, 'weights_q0.25': 0.012903370301808326, 'weights_q0.5': 0.025619198319460336, 'weights_q0.75': 0.0655794834230832, 'weights_max': 0.07669798673570015, 'weights_std': 0.02523077781016678}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 94       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 878      |\n",
      "|    total_timesteps | 118440   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.235   |\n",
      "|    critic_loss     | 1.95e-05 |\n",
      "|    ent_coef        | 1.06e-05 |\n",
      "|    ent_coef_loss   | -21.8    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.207    |\n",
      "|    mean_reward     | 0.00254  |\n",
      "|    min_reward      | -0.151   |\n",
      "|    n_updates       | 118339   |\n",
      "|    portfolio_value | 3.19     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.14955564752517192, 'max_reward': 0.20709366251631914, 'mean_reward': 0.002536032187062643, 'portfolio_value': 3.195400555698929, 'weights_q0.25': 0.011540602029595627, 'weights_q0.5': 0.028525095052978976, 'weights_q0.75': 0.06908103551832712, 'weights_max': 0.07619672449691038, 'weights_std': 0.026272793993931433}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 95       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 887      |\n",
      "|    total_timesteps | 119700   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.232   |\n",
      "|    critic_loss     | 6.09e-06 |\n",
      "|    ent_coef        | 1.04e-05 |\n",
      "|    ent_coef_loss   | -10.6    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.207    |\n",
      "|    mean_reward     | 0.00254  |\n",
      "|    min_reward      | -0.15    |\n",
      "|    n_updates       | 119599   |\n",
      "|    portfolio_value | 3.2      |\n",
      "---------------------------------\n",
      "{'min_reward': -0.1490061595854123, 'max_reward': 0.20416584848690794, 'mean_reward': 0.0026466138846011344, 'portfolio_value': 3.33473349459743, 'weights_q0.25': 0.011270730841404649, 'weights_q0.5': 0.02759816373553984, 'weights_q0.75': 0.06302720171968612, 'weights_max': 0.07330944774387291, 'weights_std': 0.02494121186292462}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 96       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 896      |\n",
      "|    total_timesteps | 120960   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.21    |\n",
      "|    critic_loss     | 9.47e-06 |\n",
      "|    ent_coef        | 1.06e-05 |\n",
      "|    ent_coef_loss   | -35.7    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.204    |\n",
      "|    mean_reward     | 0.00265  |\n",
      "|    min_reward      | -0.149   |\n",
      "|    n_updates       | 120859   |\n",
      "|    portfolio_value | 3.34     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.1578036401049765, 'max_reward': 0.20465282435433213, 'mean_reward': 0.0026169087570642267, 'portfolio_value': 3.2973050339009222, 'weights_q0.25': 0.010353578982022806, 'weights_q0.5': 0.028046653121588883, 'weights_q0.75': 0.06362250808494996, 'weights_max': 0.0734832084631087, 'weights_std': 0.025436425267957405}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 97       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 906      |\n",
      "|    total_timesteps | 122220   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.224   |\n",
      "|    critic_loss     | 3.99e-06 |\n",
      "|    ent_coef        | 1.05e-05 |\n",
      "|    ent_coef_loss   | -3.43    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.205    |\n",
      "|    mean_reward     | 0.00262  |\n",
      "|    min_reward      | -0.158   |\n",
      "|    n_updates       | 122119   |\n",
      "|    portfolio_value | 3.3      |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15377714162332246, 'max_reward': 0.20492646276681134, 'mean_reward': 0.002622258865975189, 'portfolio_value': 3.3040461711287348, 'weights_q0.25': 0.009516251652901493, 'weights_q0.5': 0.045848559959761864, 'weights_q0.75': 0.06070409854625247, 'weights_max': 0.06577516261431791, 'weights_std': 0.024575825786407875}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 98       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 915      |\n",
      "|    total_timesteps | 123480   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.229   |\n",
      "|    critic_loss     | 5.57e-06 |\n",
      "|    ent_coef        | 1.08e-05 |\n",
      "|    ent_coef_loss   | -6.36    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.205    |\n",
      "|    mean_reward     | 0.00263  |\n",
      "|    min_reward      | -0.154   |\n",
      "|    n_updates       | 123379   |\n",
      "|    portfolio_value | 3.31     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15012333703213482, 'max_reward': 0.20490203113009295, 'mean_reward': 0.002719943170653326, 'portfolio_value': 3.427128395023187, 'weights_q0.25': 0.010894990914107304, 'weights_q0.5': 0.02143475674902586, 'weights_q0.75': 0.06202376806049677, 'weights_max': 0.07511304650573002, 'weights_std': 0.02672781554444678}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 99       |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 924      |\n",
      "|    total_timesteps | 124740   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.232   |\n",
      "|    critic_loss     | 6.45e-06 |\n",
      "|    ent_coef        | 1.12e-05 |\n",
      "|    ent_coef_loss   | 13.4     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.205    |\n",
      "|    mean_reward     | 0.00272  |\n",
      "|    min_reward      | -0.15    |\n",
      "|    n_updates       | 124639   |\n",
      "|    portfolio_value | 3.43     |\n",
      "---------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'min_reward': -0.15201122424097915, 'max_reward': 0.20641783033626843, 'mean_reward': 0.0027854052228319685, 'portfolio_value': 3.509610580768279, 'weights_q0.25': 0.009930766441986975, 'weights_q0.5': 0.025923769449914737, 'weights_q0.75': 0.06548775319090627, 'weights_max': 0.07031087003870366, 'weights_std': 0.026267862974110205}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 100      |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 934      |\n",
      "|    total_timesteps | 126000   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.227   |\n",
      "|    critic_loss     | 5.56e-06 |\n",
      "|    ent_coef        | 1.12e-05 |\n",
      "|    ent_coef_loss   | 21.6     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.206    |\n",
      "|    mean_reward     | 0.00279  |\n",
      "|    min_reward      | -0.152   |\n",
      "|    n_updates       | 125899   |\n",
      "|    portfolio_value | 3.51     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.1520013290900144, 'max_reward': 0.1919502115951204, 'mean_reward': 0.0028476043153719474, 'portfolio_value': 3.5879814373686565, 'weights_q0.25': 0.00978845989007807, 'weights_q0.5': 0.017302009190736725, 'weights_q0.75': 0.06515229389623899, 'weights_max': 0.07174817467766703, 'weights_std': 0.02706662245763821}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 101      |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 943      |\n",
      "|    total_timesteps | 127260   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.219   |\n",
      "|    critic_loss     | 1.8e-05  |\n",
      "|    ent_coef        | 1.13e-05 |\n",
      "|    ent_coef_loss   | -28.2    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.192    |\n",
      "|    mean_reward     | 0.00285  |\n",
      "|    min_reward      | -0.152   |\n",
      "|    n_updates       | 127159   |\n",
      "|    portfolio_value | 3.59     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15278108836628268, 'max_reward': 0.2006229997339091, 'mean_reward': 0.002923673232937367, 'portfolio_value': 3.683828273501079, 'weights_q0.25': 0.011642561738086608, 'weights_q0.5': 0.023274744839860893, 'weights_q0.75': 0.0689541745360159, 'weights_max': 0.07606733336692029, 'weights_std': 0.026397661874162853}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 102      |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 952      |\n",
      "|    total_timesteps | 128520   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.231   |\n",
      "|    critic_loss     | 3.25e-06 |\n",
      "|    ent_coef        | 1.14e-05 |\n",
      "|    ent_coef_loss   | 2.28     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.201    |\n",
      "|    mean_reward     | 0.00293  |\n",
      "|    min_reward      | -0.153   |\n",
      "|    n_updates       | 128419   |\n",
      "|    portfolio_value | 3.69     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15072822090180293, 'max_reward': 0.20637606968461567, 'mean_reward': 0.0029008318753415115, 'portfolio_value': 3.655048162930305, 'weights_q0.25': 0.010885567756329994, 'weights_q0.5': 0.024475781411267626, 'weights_q0.75': 0.0626791743593559, 'weights_max': 0.07151906232773454, 'weights_std': 0.025270155936101067}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 103      |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 962      |\n",
      "|    total_timesteps | 129780   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.225   |\n",
      "|    critic_loss     | 5.27e-06 |\n",
      "|    ent_coef        | 1.15e-05 |\n",
      "|    ent_coef_loss   | -10.6    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.206    |\n",
      "|    mean_reward     | 0.00291  |\n",
      "|    min_reward      | -0.151   |\n",
      "|    n_updates       | 129679   |\n",
      "|    portfolio_value | 3.66     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15157637912871325, 'max_reward': 0.20193521128098424, 'mean_reward': 0.0030214843578525662, 'portfolio_value': 3.8070702908942335, 'weights_q0.25': 0.010315744792700515, 'weights_q0.5': 0.019718575736607562, 'weights_q0.75': 0.06886641000370256, 'weights_max': 0.0747246447460177, 'weights_std': 0.027443597689349713}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 104      |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 970      |\n",
      "|    total_timesteps | 131040   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.267   |\n",
      "|    critic_loss     | 5.04e-06 |\n",
      "|    ent_coef        | 1.12e-05 |\n",
      "|    ent_coef_loss   | -16.9    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.202    |\n",
      "|    mean_reward     | 0.00303  |\n",
      "|    min_reward      | -0.152   |\n",
      "|    n_updates       | 130939   |\n",
      "|    portfolio_value | 3.81     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.1507642691783021, 'max_reward': 0.20663148306838608, 'mean_reward': 0.0030300327635626473, 'portfolio_value': 3.817841282088938, 'weights_q0.25': 0.010955272653932232, 'weights_q0.5': 0.0239808232482992, 'weights_q0.75': 0.06113751969072491, 'weights_max': 0.08068172362715621, 'weights_std': 0.027253030344207656}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 105      |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 980      |\n",
      "|    total_timesteps | 132300   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.242   |\n",
      "|    critic_loss     | 4.55e-06 |\n",
      "|    ent_coef        | 1.17e-05 |\n",
      "|    ent_coef_loss   | -32.9    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.207    |\n",
      "|    mean_reward     | 0.00304  |\n",
      "|    min_reward      | -0.151   |\n",
      "|    n_updates       | 132199   |\n",
      "|    portfolio_value | 3.82     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.1496531413700438, 'max_reward': 0.20667241832967898, 'mean_reward': 0.0031195664814767537, 'portfolio_value': 3.9306537666607104, 'weights_q0.25': 0.010029607084453426, 'weights_q0.5': 0.023747777233468163, 'weights_q0.75': 0.0680900000197496, 'weights_max': 0.07500462798866689, 'weights_std': 0.02656536423957935}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 106      |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 989      |\n",
      "|    total_timesteps | 133560   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.224   |\n",
      "|    critic_loss     | 4.51e-06 |\n",
      "|    ent_coef        | 1.16e-05 |\n",
      "|    ent_coef_loss   | 1.61     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.207    |\n",
      "|    mean_reward     | 0.00312  |\n",
      "|    min_reward      | -0.15    |\n",
      "|    n_updates       | 133459   |\n",
      "|    portfolio_value | 3.93     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15084547790081324, 'max_reward': 0.2064957353115707, 'mean_reward': 0.0030693760555972386, 'portfolio_value': 3.8674138300525174, 'weights_q0.25': 0.010393350871004695, 'weights_q0.5': 0.03300707597599489, 'weights_q0.75': 0.061546034408972806, 'weights_max': 0.07045496895188842, 'weights_std': 0.024322757513425233}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 107      |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 999      |\n",
      "|    total_timesteps | 134820   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.233   |\n",
      "|    critic_loss     | 2.15e-06 |\n",
      "|    ent_coef        | 1.15e-05 |\n",
      "|    ent_coef_loss   | -15      |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.206    |\n",
      "|    mean_reward     | 0.00307  |\n",
      "|    min_reward      | -0.151   |\n",
      "|    n_updates       | 134719   |\n",
      "|    portfolio_value | 3.87     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15522333095351226, 'max_reward': 0.20517959052171225, 'mean_reward': 0.0031799906985699174, 'portfolio_value': 4.006788280198095, 'weights_q0.25': 0.01041762742471661, 'weights_q0.5': 0.02421630117310186, 'weights_q0.75': 0.0622320807821818, 'weights_max': 0.07605461411441974, 'weights_std': 0.025884974174327807}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 108      |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 1008     |\n",
      "|    total_timesteps | 136080   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.243   |\n",
      "|    critic_loss     | 9.66e-06 |\n",
      "|    ent_coef        | 1.15e-05 |\n",
      "|    ent_coef_loss   | 38.7     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.205    |\n",
      "|    mean_reward     | 0.00319  |\n",
      "|    min_reward      | -0.155   |\n",
      "|    n_updates       | 135979   |\n",
      "|    portfolio_value | 4.01     |\n",
      "---------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'min_reward': -0.15723847207131708, 'max_reward': 0.20682796125848804, 'mean_reward': 0.0030969816821295195, 'portfolio_value': 3.9021969194831927, 'weights_q0.25': 0.013357503530440865, 'weights_q0.5': 0.025289437560607888, 'weights_q0.75': 0.0592321564441895, 'weights_max': 0.07778071011439174, 'weights_std': 0.024697629020929583}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 109      |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 1018     |\n",
      "|    total_timesteps | 137340   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.247   |\n",
      "|    critic_loss     | 5.31e-06 |\n",
      "|    ent_coef        | 1.11e-05 |\n",
      "|    ent_coef_loss   | -1.17    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.207    |\n",
      "|    mean_reward     | 0.0031   |\n",
      "|    min_reward      | -0.157   |\n",
      "|    n_updates       | 137239   |\n",
      "|    portfolio_value | 3.9      |\n",
      "---------------------------------\n",
      "{'min_reward': -0.1568534224981808, 'max_reward': 0.20829063005969384, 'mean_reward': 0.0030781824223199464, 'portfolio_value': 3.878509852123132, 'weights_q0.25': 0.01045971976296493, 'weights_q0.5': 0.025944206544306035, 'weights_q0.75': 0.06291542237348749, 'weights_max': 0.0662649837311772, 'weights_std': 0.024133348801622264}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 110      |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 1027     |\n",
      "|    total_timesteps | 138600   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.241   |\n",
      "|    critic_loss     | 2.33e-06 |\n",
      "|    ent_coef        | 1.1e-05  |\n",
      "|    ent_coef_loss   | 33.1     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.208    |\n",
      "|    mean_reward     | 0.00308  |\n",
      "|    min_reward      | -0.157   |\n",
      "|    n_updates       | 138499   |\n",
      "|    portfolio_value | 3.88     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.16118245701699965, 'max_reward': 0.207004118671654, 'mean_reward': 0.0030928351673791977, 'portfolio_value': 3.8969723108977914, 'weights_q0.25': 0.010682815820884154, 'weights_q0.5': 0.03753038667504842, 'weights_q0.75': 0.06307319056897806, 'weights_max': 0.07094555793670168, 'weights_std': 0.024208932834069607}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 111      |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 1036     |\n",
      "|    total_timesteps | 139860   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.256   |\n",
      "|    critic_loss     | 5.51e-06 |\n",
      "|    ent_coef        | 1.14e-05 |\n",
      "|    ent_coef_loss   | 7.34     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.207    |\n",
      "|    mean_reward     | 0.0031   |\n",
      "|    min_reward      | -0.161   |\n",
      "|    n_updates       | 139759   |\n",
      "|    portfolio_value | 3.9      |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15329726282852796, 'max_reward': 0.20636616555044185, 'mean_reward': 0.003141570983273072, 'portfolio_value': 3.9583794389240685, 'weights_q0.25': 0.010031623093352681, 'weights_q0.5': 0.027337666467359887, 'weights_q0.75': 0.06270654914295708, 'weights_max': 0.07133118053666972, 'weights_std': 0.024878462744875134}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 112      |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 1046     |\n",
      "|    total_timesteps | 141120   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.232   |\n",
      "|    critic_loss     | 6.27e-06 |\n",
      "|    ent_coef        | 1.12e-05 |\n",
      "|    ent_coef_loss   | -15.7    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.206    |\n",
      "|    mean_reward     | 0.00315  |\n",
      "|    min_reward      | -0.153   |\n",
      "|    n_updates       | 141019   |\n",
      "|    portfolio_value | 3.96     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15286654612034978, 'max_reward': 0.19638047285996407, 'mean_reward': 0.003114256251489457, 'portfolio_value': 3.923962876876713, 'weights_q0.25': 0.009946195626867715, 'weights_q0.5': 0.03189908365341624, 'weights_q0.75': 0.060186783913934006, 'weights_max': 0.06922142497534645, 'weights_std': 0.024301036221320856}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 113      |\n",
      "|    fps             | 134      |\n",
      "|    time_elapsed    | 1055     |\n",
      "|    total_timesteps | 142380   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.251   |\n",
      "|    critic_loss     | 2.55e-06 |\n",
      "|    ent_coef        | 1.11e-05 |\n",
      "|    ent_coef_loss   | 7.96     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.196    |\n",
      "|    mean_reward     | 0.00312  |\n",
      "|    min_reward      | -0.153   |\n",
      "|    n_updates       | 142279   |\n",
      "|    portfolio_value | 3.93     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.1552243106653372, 'max_reward': 0.11549147471922692, 'mean_reward': 0.002920801735354732, 'portfolio_value': 3.680210186546962, 'weights_q0.25': 0.011934528450348886, 'weights_q0.5': 0.028020741855901376, 'weights_q0.75': 0.052613365665381016, 'weights_max': 0.08149336137530672, 'weights_std': 0.024649372532578373}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 114      |\n",
      "|    fps             | 135      |\n",
      "|    time_elapsed    | 1063     |\n",
      "|    total_timesteps | 143640   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.258   |\n",
      "|    critic_loss     | 1.12e-05 |\n",
      "|    ent_coef        | 1.14e-05 |\n",
      "|    ent_coef_loss   | 4.03     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.115    |\n",
      "|    mean_reward     | 0.00292  |\n",
      "|    min_reward      | -0.155   |\n",
      "|    n_updates       | 143539   |\n",
      "|    portfolio_value | 3.68     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15150339730250445, 'max_reward': 0.19166211087441698, 'mean_reward': 0.003138032771812178, 'portfolio_value': 3.9539212924833467, 'weights_q0.25': 0.011472630503006086, 'weights_q0.5': 0.026395169679883736, 'weights_q0.75': 0.06480068571807615, 'weights_max': 0.07345299409728838, 'weights_std': 0.025203758757733633}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 115      |\n",
      "|    fps             | 135      |\n",
      "|    time_elapsed    | 1072     |\n",
      "|    total_timesteps | 144900   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.258   |\n",
      "|    critic_loss     | 6.71e-06 |\n",
      "|    ent_coef        | 1.11e-05 |\n",
      "|    ent_coef_loss   | 19.6     |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.192    |\n",
      "|    mean_reward     | 0.00314  |\n",
      "|    min_reward      | -0.152   |\n",
      "|    n_updates       | 144799   |\n",
      "|    portfolio_value | 3.96     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15273087550223863, 'max_reward': 0.2048335649416024, 'mean_reward': 0.003147869504977318, 'portfolio_value': 3.9663155762714206, 'weights_q0.25': 0.01224255765058622, 'weights_q0.5': 0.032733599375104006, 'weights_q0.75': 0.05956378228793439, 'weights_max': 0.07406896281908797, 'weights_std': 0.023971256689344026}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 116      |\n",
      "|    fps             | 135      |\n",
      "|    time_elapsed    | 1081     |\n",
      "|    total_timesteps | 146160   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.248   |\n",
      "|    critic_loss     | 5.56e-06 |\n",
      "|    ent_coef        | 1.05e-05 |\n",
      "|    ent_coef_loss   | -36.4    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.205    |\n",
      "|    mean_reward     | 0.00315  |\n",
      "|    min_reward      | -0.153   |\n",
      "|    n_updates       | 146059   |\n",
      "|    portfolio_value | 3.97     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.149503820972201, 'max_reward': 0.20691499580181794, 'mean_reward': 0.003201468949485879, 'portfolio_value': 4.033850876352208, 'weights_q0.25': 0.011032611846622105, 'weights_q0.5': 0.03451265592828605, 'weights_q0.75': 0.05816403949278387, 'weights_max': 0.06702146821307939, 'weights_std': 0.02205719387765062}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 117      |\n",
      "|    fps             | 135      |\n",
      "|    time_elapsed    | 1090     |\n",
      "|    total_timesteps | 147420   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.259   |\n",
      "|    critic_loss     | 4.38e-06 |\n",
      "|    ent_coef        | 1.1e-05  |\n",
      "|    ent_coef_loss   | -9.09    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.207    |\n",
      "|    mean_reward     | 0.00321  |\n",
      "|    min_reward      | -0.15    |\n",
      "|    n_updates       | 147319   |\n",
      "|    portfolio_value | 4.04     |\n",
      "---------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'min_reward': -0.15143566937644126, 'max_reward': 0.20675489053806198, 'mean_reward': 0.00328807773730698, 'portfolio_value': 4.142977949006797, 'weights_q0.25': 0.010930639477993182, 'weights_q0.5': 0.025832406966625525, 'weights_q0.75': 0.0634993178762739, 'weights_max': 0.07523004119948225, 'weights_std': 0.024923456300297974}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 118      |\n",
      "|    fps             | 135      |\n",
      "|    time_elapsed    | 1099     |\n",
      "|    total_timesteps | 148680   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.272   |\n",
      "|    critic_loss     | 5.43e-06 |\n",
      "|    ent_coef        | 1.1e-05  |\n",
      "|    ent_coef_loss   | -15.1    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.207    |\n",
      "|    mean_reward     | 0.00329  |\n",
      "|    min_reward      | -0.151   |\n",
      "|    n_updates       | 148579   |\n",
      "|    portfolio_value | 4.15     |\n",
      "---------------------------------\n",
      "{'min_reward': -0.15388075259987127, 'max_reward': 0.20591192147887205, 'mean_reward': 0.0031857034198440534, 'portfolio_value': 4.013986309003509, 'weights_q0.25': 0.01374520782929494, 'weights_q0.5': 0.03154101408944092, 'weights_q0.75': 0.05577793712321944, 'weights_max': 0.0744673429414178, 'weights_std': 0.022489091591756757}\n",
      "---------------------------------\n",
      "| time/              |          |\n",
      "|    episodes        | 119      |\n",
      "|    fps             | 135      |\n",
      "|    time_elapsed    | 1108     |\n",
      "|    total_timesteps | 149940   |\n",
      "| train/             |          |\n",
      "|    actor_loss      | -0.254   |\n",
      "|    critic_loss     | 2.53e-06 |\n",
      "|    ent_coef        | 1.12e-05 |\n",
      "|    ent_coef_loss   | -4.44    |\n",
      "|    learning_rate   | 0.001    |\n",
      "|    max_reward      | 0.206    |\n",
      "|    mean_reward     | 0.00319  |\n",
      "|    min_reward      | -0.154   |\n",
      "|    n_updates       | 149839   |\n",
      "|    portfolio_value | 4.01     |\n",
      "---------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<stable_baselines3.sac.sac.SAC at 0x7f149123cd00>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_name = 'SAC-mr-re-256-256-corrs-dw'\n",
    "model.learn(total_timesteps=150_000, progress_bar=False, log_interval=1,\n",
    "            reset_num_timesteps=False, tb_log_name=model_name,\n",
    "            callback=MeanRewardCallback())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Saving model and running mean from env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "is_executing": true
   },
   "outputs": [],
   "source": [
    "model.save('models/' + model_name)\n",
    "with open('models/' + model_name + '_obs_rms.pickle', 'wb') as handle:\n",
    "    pickle.dump(train_env.obs_rms, handle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "is_executing": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'min_reward': -0.1551756418967425, 'max_reward': 0.2051501027367493, 'mean_reward': 0.0031182704246739334, 'portfolio_value': 3.9290207350891557, 'weights_q0.25': 0.012947083196902343, 'weights_q0.5': 0.02831708697868826, 'weights_q0.75': 0.06261019890797306, 'weights_max': 0.07241900651081343, 'weights_std': 0.023459503237995828}\n",
      "{'min_reward': -0.15261769454163304, 'max_reward': 0.2067505208626652, 'mean_reward': 0.0031151472645706093, 'portfolio_value': 3.9250855533589637, 'weights_q0.25': 0.011957855735723659, 'weights_q0.5': 0.023034958578173094, 'weights_q0.75': 0.06179814493697238, 'weights_max': 0.06960267782515266, 'weights_std': 0.024291828874461626}\n",
      "{'min_reward': -0.1558077356194606, 'max_reward': 0.2057931701088344, 'mean_reward': 0.003103618810809518, 'portfolio_value': 3.9105597016199978, 'weights_q0.25': 0.013518319494265381, 'weights_q0.5': 0.02403929192007034, 'weights_q0.75': 0.0625736048484683, 'weights_max': 0.07221934615278286, 'weights_std': 0.023597901522330263}\n",
      "{'min_reward': -0.15602281915579524, 'max_reward': 0.206046187098488, 'mean_reward': 0.003090738164612721, 'portfolio_value': 3.8943300874120292, 'weights_q0.25': 0.011339843956047391, 'weights_q0.5': 0.03796492147610378, 'weights_q0.75': 0.06000663309399026, 'weights_max': 0.06611584686816623, 'weights_std': 0.022304349607803056}\n",
      "{'min_reward': -0.1561554402537531, 'max_reward': 0.20652975639206872, 'mean_reward': 0.003111264323777839, 'portfolio_value': 3.92019304796008, 'weights_q0.25': 0.010307512922548489, 'weights_q0.5': 0.04084531877492793, 'weights_q0.75': 0.05597973125967974, 'weights_max': 0.06476738415215771, 'weights_std': 0.02172966940061143}\n",
      "3.915837836070159 0.012399351794641649\n"
     ]
    }
   ],
   "source": [
    "mean_reward, std_reward = evaluate_policy(model, train_env, n_eval_episodes=5, deterministic=False, warn=False, return_episode_rewards=False)\n",
    "print(mean_reward, std_reward)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "is_executing": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'stock_dim', 'close_prices_pct', 'tickers', 'n_assets', 'objective', 'risk_measure', 'rewards_memory', 'actions_memory', 'portfolio_value', 'observations_range', 'current_step', 'weights', 'reward_range', 'action_space', 'observation_space'])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_env.venv.venv.envs[0].__dict__.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "is_executing": true
   },
   "outputs": [],
   "source": [
    "train_env.training = False\n",
    "train_env.venv.venv.envs[0].observations_range = (0, 1760) # change to use the test period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "is_executing": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "{'min_reward': -0.021275502604930344, 'max_reward': -0.004746610762858191, 'mean_reward': -0.013011056683894268, 'portfolio_value': -0.026022113367788535, 'weights_q0.25': 0.013377119957668228, 'weights_q0.5': 0.031773963374719216, 'weights_q0.75': 0.05709768210254099, 'weights_max': 0.06952922760609104, 'weights_std': 0.022144421160463296}\n",
      "{'min_reward': -0.021275502604930344, 'max_reward': -0.004746610762858191, 'mean_reward': -0.011356544208212463, 'portfolio_value': -0.034069632624637386, 'weights_q0.25': 0.012924303139651544, 'weights_q0.5': 0.029190664871458286, 'weights_q0.75': 0.06026878612609565, 'weights_max': 0.07495704238045953, 'weights_std': 0.023314617475023787}\n",
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      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.0023112207436297052, 'portfolio_value': 4.019212873172064, 'weights_q0.25': 0.014624249058905272, 'weights_q0.5': 0.029251289724333704, 'weights_q0.75': 0.04987276209728867, 'weights_max': 0.08709410864847376, 'weights_std': 0.022927546648050637}\n",
      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.0023083254756661987, 'portfolio_value': 4.016486327659193, 'weights_q0.25': 0.017128038217329437, 'weights_q0.5': 0.023569547045426548, 'weights_q0.75': 0.04309204088893635, 'weights_max': 0.0931530757275557, 'weights_std': 0.025065385761111628}\n",
      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.0023038647312580624, 'portfolio_value': 4.011028497120293, 'weights_q0.25': 0.014344668436434293, 'weights_q0.5': 0.030115539784078604, 'weights_q0.75': 0.05209304367663013, 'weights_max': 0.08350679764186009, 'weights_std': 0.022768419533786798}\n",
      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.002291739680473456, 'portfolio_value': 3.992210523384767, 'weights_q0.25': 0.0163556169130619, 'weights_q0.5': 0.0251880800399694, 'weights_q0.75': 0.05641954809837345, 'weights_max': 0.08589778860127933, 'weights_std': 0.022553725981237994}\n",
      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.002284554321676824, 'portfolio_value': 3.981978182682711, 'weights_q0.25': 0.01600793402678733, 'weights_q0.5': 0.0282959260050365, 'weights_q0.75': 0.05388174706935914, 'weights_max': 0.07657596406225013, 'weights_std': 0.02057748727051191}\n",
      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.0022847798086451402, 'portfolio_value': 3.9846559862771316, 'weights_q0.25': 0.017040916785338797, 'weights_q0.5': 0.02480865075483182, 'weights_q0.75': 0.04605773711554316, 'weights_max': 0.090286129771881, 'weights_std': 0.02391230046470434}\n",
      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.0022873358363247015, 'portfolio_value': 3.9914010343866106, 'weights_q0.25': 0.014130641690418345, 'weights_q0.5': 0.02997383503576794, 'weights_q0.75': 0.05264877589548275, 'weights_max': 0.08616072431553178, 'weights_std': 0.024211608900003515}\n",
      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.0022844945432527602, 'portfolio_value': 3.988727472519326, 'weights_q0.25': 0.018026597834408204, 'weights_q0.5': 0.023968750003884338, 'weights_q0.75': 0.04141405626301863, 'weights_max': 0.09415809758101461, 'weights_std': 0.025502234174740818}\n",
      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.002288571933907847, 'portfolio_value': 3.9981351685370146, 'weights_q0.25': 0.01668238774618013, 'weights_q0.5': 0.025481750969141927, 'weights_q0.75': 0.059058163513085335, 'weights_max': 0.07524931343016716, 'weights_std': 0.023039754128571455}\n",
      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.0022978153018882925, 'portfolio_value': 4.016581147700741, 'weights_q0.25': 0.014831070953739693, 'weights_q0.5': 0.024055645526226563, 'weights_q0.75': 0.0567181182451221, 'weights_max': 0.09109829550233499, 'weights_std': 0.02565591643285984}\n",
      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.002288225227769186, 'portfolio_value': 4.002105923368312, 'weights_q0.25': 0.01402435025709376, 'weights_q0.5': 0.025146421081752385, 'weights_q0.75': 0.06046351096173216, 'weights_max': 0.07886285667222317, 'weights_std': 0.02366974675409976}\n",
      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.002279457568023395, 'portfolio_value': 3.9890507440409473, 'weights_q0.25': 0.016029055129459898, 'weights_q0.5': 0.03448414155230663, 'weights_q0.75': 0.04960866643523672, 'weights_max': 0.07640262628555433, 'weights_std': 0.020114593230964362}\n",
      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.0022619528047776467, 'portfolio_value': 3.9606793611656648, 'weights_q0.25': 0.017395430088162946, 'weights_q0.5': 0.03146255225581145, 'weights_q0.75': 0.04948410999617296, 'weights_max': 0.07873505289792482, 'weights_std': 0.020585802798713988}\n",
      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.002253069105167566, 'portfolio_value': 3.947377072253582, 'weights_q0.25': 0.015849671809661924, 'weights_q0.5': 0.03213305275029356, 'weights_q0.75': 0.045306135082340736, 'weights_max': 0.08718839830453012, 'weights_std': 0.02277002694241267}\n",
      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.002250675949309896, 'portfolio_value': 3.9454349391402532, 'weights_q0.25': 0.018227527825530908, 'weights_q0.5': 0.025677056857589482, 'weights_q0.75': 0.04778140510059825, 'weights_max': 0.09394565260377635, 'weights_std': 0.023529118239126495}\n",
      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.002252675231456547, 'portfolio_value': 3.95119235597479, 'weights_q0.25': 0.016886173234453093, 'weights_q0.5': 0.028569694625914424, 'weights_q0.75': 0.05059012714783234, 'weights_max': 0.07769085666889129, 'weights_std': 0.021608629640365827}\n",
      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.0022497345534956744, 'portfolio_value': 3.9482841413849146, 'weights_q0.25': 0.014153637031834343, 'weights_q0.5': 0.030006410650421284, 'weights_q0.75': 0.050585958065663426, 'weights_max': 0.08860770730738181, 'weights_std': 0.0239433240237194}\n",
      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.0022536461920428575, 'portfolio_value': 3.957402713227264, 'weights_q0.25': 0.016250530815303525, 'weights_q0.5': 0.023012076372268334, 'weights_q0.75': 0.046431195784840736, 'weights_max': 0.09562861192914442, 'weights_std': 0.02587445633719945}\n",
      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.002251656248403366, 'portfolio_value': 3.9561600284447196, 'weights_q0.25': 0.01797462736541471, 'weights_q0.5': 0.024663131506365475, 'weights_q0.75': 0.05754251845114652, 'weights_max': 0.08779696477514412, 'weights_std': 0.023140589858996502}\n",
      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.002240351686918245, 'portfolio_value': 3.9385382656022805, 'weights_q0.25': 0.018211249810945976, 'weights_q0.5': 0.02705486761452485, 'weights_q0.75': 0.049914721522211936, 'weights_max': 0.09203754184195699, 'weights_std': 0.02180102034506281}\n",
      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.0022504001726970655, 'portfolio_value': 3.958453903774144, 'weights_q0.25': 0.017177437952545, 'weights_q0.5': 0.026099733843624885, 'weights_q0.75': 0.041512006373404825, 'weights_max': 0.1063574213037701, 'weights_std': 0.02511415713454569}\n",
      "{'min_reward': -0.15280873134165546, 'max_reward': 0.20630300645779137, 'mean_reward': 0.0022442573624851378, 'portfolio_value': 3.9498929579738484, 'weights_q0.25': 0.015756707263401316, 'weights_q0.5': 0.03031222250343006, 'weights_q0.75': 0.05486201880525693, 'weights_max': 0.07649744631776383, 'weights_std': 0.0222200401895222}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1760, 1760)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import copy\n",
    "\n",
    "obs = train_env.reset()\n",
    "weights_states = [train_env.venv.venv.envs[0].weights]\n",
    "portfolio_values = [0]\n",
    "while True:\n",
    "    action, _states = model.predict(obs)\n",
    "    obs, rewards, done, info = train_env.step(action)\n",
    "    if done:\n",
    "        break\n",
    "    else:\n",
    "        weights_states.append(copy.deepcopy(train_env.venv.venv.envs[0].weights))\n",
    "        portfolio_values.append(train_env.venv.venv.envs[0].portfolio_value)\n",
    "        print(train_env.render())\n",
    "len(portfolio_values), len(weights_states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "is_executing": true
   },
   "outputs": [],
   "source": [
    "portfolio_state = pd.DataFrame(index=train_env.venv.venv.envs[0].close_prices_pct.index, data=portfolio_values)\n",
    "portfolio_state.to_csv('portfolios/' + model_name + '_portfolio.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "is_executing": true
   },
   "outputs": [],
   "source": [
    "weights_df = pd.DataFrame(index=train_env.venv.venv.envs[0].close_prices_pct.columns, data=weights_states[-1])\n",
    "weights_df.to_csv('portfolios/' + model_name + '_weights.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "is_executing": true
   },
   "source": [
    "## Tests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-12T18:09:36.231341632Z",
     "start_time": "2023-05-12T18:09:36.211779731Z"
    }
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'data/sp500_data_5y.pickle'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[294], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdata/sp500_data_5y.pickle\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m      2\u001b[0m     data \u001b[38;5;241m=\u001b[39m pickle\u001b[38;5;241m.\u001b[39mload(f)\n\u001b[1;32m      3\u001b[0m     close_prices \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({k: v[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClose\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m data\u001b[38;5;241m.\u001b[39mitems()})\n",
      "File \u001b[0;32m~/miniconda3/lib/python3.10/site-packages/IPython/core/interactiveshell.py:284\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m    277\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[1;32m    278\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    279\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    280\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    281\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    282\u001b[0m     )\n\u001b[0;32m--> 284\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mio_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'data/sp500_data_5y.pickle'"
     ]
    }
   ],
   "source": [
    "with open('data/sp500_data_5y.pickle', 'rb') as f:\n",
    "    data = pickle.load(f)\n",
    "    close_prices = pd.DataFrame({k: v['Close'] for k, v in data.items()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['AAPL'].diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rand_w = softmax_normalization(np.random.randn(489))\n",
    "rand_w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ones_w = softmax_normalization(np.ones(489))\n",
    "ones_w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "close_prices.diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(close_prices.pct_change().iloc[48] @ rand_w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "close_prices.pct_change().dropna().mean() @ rand_w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(close_prices.pct_change().dropna() @ rand_w).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(close_prices.pct_change().dropna() @ ones_w).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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