DeepQLearning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install gym -U"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "sdThOsJP_4J-",
        "outputId": "37954fa9-9a22-4d28-f637-3cbc885dd96d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: gym in /usr/local/lib/python3.10/dist-packages (0.25.2)\n",
            "Collecting gym\n",
            "  Downloading gym-0.26.2.tar.gz (721 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/721.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m721.7/721.7 kB\u001b[0m \u001b[31m36.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.10/dist-packages (from gym) (1.22.4)\n",
            "Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from gym) (2.2.1)\n",
            "Requirement already satisfied: gym-notices>=0.0.4 in /usr/local/lib/python3.10/dist-packages (from gym) (0.0.8)\n",
            "Building wheels for collected packages: gym\n",
            "  Building wheel for gym (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gym: filename=gym-0.26.2-py3-none-any.whl size=827627 sha256=12dada5d149d31699b64400a10ebc3bb56d1105a64bc41a0135eae82f745243a\n",
            "  Stored in directory: /root/.cache/pip/wheels/b9/22/6d/3e7b32d98451b4cd9d12417052affbeeeea012955d437da1da\n",
            "Successfully built gym\n",
            "Installing collected packages: gym\n",
            "  Attempting uninstall: gym\n",
            "    Found existing installation: gym 0.25.2\n",
            "    Uninstalling gym-0.25.2:\n",
            "      Successfully uninstalled gym-0.25.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "dopamine-rl 4.0.6 requires gym<=0.25.2, but you have gym 0.26.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed gym-0.26.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "gym"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w_qB8QVs_pn5",
        "outputId": "26a433bc-48ea-4946-eb41-2d446e60e71d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mA saída de streaming foi truncada nas últimas 5000 linhas.\u001b[0m\n",
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            "Reward: 162.0, Total episodes: 167\n",
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            "2/2 [==============================] - 0s 5ms/step - loss: 1.7797\n",
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            "Reward: 169.0, Total episodes: 168\n",
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            "2/2 [==============================] - 0s 11ms/step - loss: 0.0310\n",
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            "2/2 [==============================] - 0s 6ms/step - loss: 0.8378\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.0521\n",
            "Reward: 209.0, Total episodes: 169\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0563\n",
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            "2/2 [==============================] - 0s 6ms/step - loss: 5.3179\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0466\n",
            "Reward: 322.0, Total episodes: 170\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.3443\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.0236\n",
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            "2/2 [==============================] - 0s 6ms/step - loss: 0.0358\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.4092\n",
            "Reward: 114.0, Total episodes: 171\n",
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            "Reward: 304.0, Total episodes: 172\n",
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            "Reward: 213.0, Total episodes: 173\n",
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            "2/2 [==============================] - 0s 6ms/step - loss: 0.9013\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.0979\n",
            "Reward: 241.0, Total episodes: 174\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0784\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 1.0126\n",
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            "2/2 [==============================] - 0s 6ms/step - loss: 0.0841\n",
            "2/2 [==============================] - 0s 8ms/step - loss: 0.0802\n",
            "Reward: 128.0, Total episodes: 175\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.1074\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.0489\n",
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            "2/2 [==============================] - 0s 6ms/step - loss: 0.0474\n",
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            "Reward: 20.0, Total episodes: 176\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 0.9298\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 4.0587\n",
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            "Reward: 247.0, Total episodes: 177\n",
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            "Modelo chegou ao objetivo!\n",
            "2/2 [==============================] - 0s 7ms/step - loss: 0.0243\n",
            "Reward: 500.0, Total episodes: 178\n",
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            "Reward: 188.0, Total episodes: 179\n",
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            "Reward: 436.0, Total episodes: 180\n",
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            "2/2 [==============================] - 0s 5ms/step - loss: 2.0678\n",
            "2/2 [==============================] - 0s 6ms/step - loss: 0.2245\n",
            "Reward: 204.0, Total episodes: 181\n",
            "2/2 [==============================] - 0s 9ms/step - loss: 2.0148\n",
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            "Reward: 263.0, Total episodes: 182\n",
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            "Reward: 334.0, Total episodes: 183\n",
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            "Reward: 191.0, Total episodes: 184\n",
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            "Reward: 246.0, Total episodes: 185\n",
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            "2/2 [==============================] - 0s 6ms/step - loss: 0.0295\n",
            "2/2 [==============================] - 0s 11ms/step - loss: 0.0331\n",
            "Modelo chegou ao objetivo!\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
          ]
        }
      ],
      "source": [
        "import gym\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import random\n",
        "from collections import deque\n",
        "from skimage.color import rgb2gray\n",
        "from skimage.transform import resize\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import gc\n",
        "\n",
        "def QNeuralNetwork(InputShape, OutputShape):\n",
        "    model = tf.keras.Sequential([\n",
        "        tf.keras.layers.Dense(32, activation='relu', input_shape=InputShape),\n",
        "        tf.keras.layers.Dense(32, activation='relu'),\n",
        "        tf.keras.layers.Dense(OutputShape, activation='linear')\n",
        "    ])\n",
        "    return model\n",
        "\n",
        "def PreProcess(frame):\n",
        "    im = rgb2gray(frame)\n",
        "    normalized_frame = im/255.0\n",
        "    resized_frame = resize(normalized_frame, [110, 84])\n",
        "    return np.float32(resized_frame)\n",
        "\n",
        "EPSILON = 1.000\n",
        "EPSILON_DECAY = 0.999\n",
        "EPSILON_MIN = 0.001\n",
        "GAMMA = 0.950\n",
        "BATCH_SIZE = 64\n",
        "LEARNING_RATE = 0.001\n",
        "MEMORY_SIZE = 5000\n",
        "\n",
        "states, actions, rewards, next_states, terminateds = deque(maxlen=MEMORY_SIZE), deque(maxlen=MEMORY_SIZE), deque(maxlen=MEMORY_SIZE), deque(maxlen=MEMORY_SIZE), deque(maxlen=MEMORY_SIZE)\n",
        "\n",
        "env = gym.make(\"CartPole-v1\")\n",
        "\n",
        "state, info = env.reset()\n",
        "\n",
        "model = QNeuralNetwork(env.observation_space.shape, env.action_space.n)\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE)\n",
        "model.compile(optimizer=optimizer, loss='mse')\n",
        "\n",
        "checkpoint_path = \"training_2/my_model\"\n",
        "#model = tf.keras.models.load_model(checkpoint_path)\n",
        "\n",
        "frame_count = 0\n",
        "total_reward = 0\n",
        "Total_episodes = 0\n",
        "TimesObjectiveReached = 0\n",
        "\n",
        "IsTesting = False\n",
        "\n",
        "while True:\n",
        "    frame_count += 1\n",
        "    randNum = np.random.rand()\n",
        "    action = 0\n",
        "    if randNum > EPSILON or IsTesting:\n",
        "        q_values = model(tf.convert_to_tensor(np.array([state]))).numpy()\n",
        "        action = np.argmax(q_values[0])\n",
        "    else:\n",
        "        action = env.action_space.sample()\n",
        "\n",
        "    next_state, reward, terminated, truncated, info = env.step(action)\n",
        "\n",
        "    states.append(state)\n",
        "    actions.append(action)\n",
        "    rewards.append(reward)\n",
        "    next_states.append(next_state)\n",
        "    terminateds.append(terminated)\n",
        "    state = np.copy(next_state)\n",
        "    total_reward += reward\n",
        "\n",
        "    if total_reward >= 500 and not IsTesting:\n",
        "        print(\"Modelo chegou ao objetivo!\")\n",
        "        TimesObjectiveReached += 1\n",
        "        if TimesObjectiveReached >= 3:\n",
        "            model.save(checkpoint_path)\n",
        "            break\n",
        "\n",
        "    if len(states) > BATCH_SIZE and not IsTesting:\n",
        "        batch_index = np.random.choice(len(states), BATCH_SIZE)\n",
        "        batch_states = np.array([states[i] for i in batch_index])\n",
        "        batch_actions = np.array([actions[i] for i in batch_index])\n",
        "        batch_rewards = np.array([rewards[i] for i in batch_index])\n",
        "        batch_next_states = np.array([next_states[i] for i in batch_index])\n",
        "        batch_terminateds = np.array([terminateds[i] for i in batch_index])\n",
        "        q_values = model(tf.convert_to_tensor(batch_states)).numpy()\n",
        "        q_values_next = model(tf.convert_to_tensor(batch_next_states)).numpy()\n",
        "        batch_index = np.arange(BATCH_SIZE, dtype=np.int32)\n",
        "        q_target = np.copy(q_values)\n",
        "        q_target[batch_index, batch_actions] = batch_rewards + GAMMA * np.max(q_values_next, axis=1) * (1 - batch_terminateds)\n",
        "        model.fit(tf.convert_to_tensor(batch_states), tf.convert_to_tensor(q_target), epochs=1, verbose=1)\n",
        "        # \"Solve\" memory leak\n",
        "        #tf.keras.backend.clear_session()\n",
        "        #gc.collect()\n",
        "\n",
        "    if EPSILON > EPSILON_MIN:\n",
        "        EPSILON *= EPSILON_DECAY\n",
        "    if terminated or truncated:\n",
        "        Total_episodes += 1\n",
        "        print(f\"Reward: {total_reward}, Total episodes: {Total_episodes}\")\n",
        "        total_reward = 0\n",
        "        observation, info = env.reset()\n",
        "\n",
        "env.close()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -r file.zip ./training_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g6Vs4y1yNh7N",
        "outputId": "b4265c59-8bb2-49e6-b822-c48dbb36afa9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: training_2/ (stored 0%)\n",
            "  adding: training_2/my_model/ (stored 0%)\n",
            "  adding: training_2/my_model/fingerprint.pb (stored 0%)\n",
            "  adding: training_2/my_model/saved_model.pb (deflated 88%)\n",
            "  adding: training_2/my_model/variables/ (stored 0%)\n",
            "  adding: training_2/my_model/variables/variables.index (deflated 59%)\n",
            "  adding: training_2/my_model/variables/variables.data-00000-of-00001 (deflated 23%)\n",
            "  adding: training_2/my_model/assets/ (stored 0%)\n",
            "  adding: training_2/my_model/keras_metadata.pb (deflated 87%)\n"
          ]
        }
      ]
    }
  ]
}