metalearner_ldm.py

import os
import time

import gym
import numpy as np
np.set_printoptions(suppress=True)
import torch

from algorithms.a2c import A2C
from algorithms.online_storage import OnlineStorage
from algorithms.ppo import PPO
from environments.parallel_envs import make_vec_envs
from models.policy import Policy
from utils import evaluation as utl_eval
from utils import helpers as utl
from utils.tb_logger import TBLogger
from vae import VaribadVAE

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print('metalearner_ldm loaded')


class MetaLearner:
    """
    Meta-Learner class with the main training loop for variBAD.
    """

    def __init__(self, args1, args2):
        self.args1 = args1 #LDM args used for latent dynamics network of LDM
        self.args2 = args2 #RL2 args used for policy network of LDM
        self.eval_task_num = self.args1.eval_task_num
        utl.seed(self.args2.seed, self.args2.deterministic_execution)

        # count number of frames and number of meta-iterations
        self.frames = 0
        self.iter_idx = 0

        # initialise tensorboard logger
        self.logger = TBLogger(self.args2, self.args1.exp_label)

        # initialise environments
        self.envs = make_vec_envs(env_name=args2.env_name, seed=args2.seed, num_processes=args2.num_processes,
                                  gamma=args2.policy_gamma, log_dir=args2.agent_log_dir, device=device,
                                  allow_early_resets=False,
                                  episodes_per_task=self.args2.max_rollouts_per_task,
                                  obs_rms=None, ret_rms=None,
                                  )

        # calculate what the maximum length of the trajectories is
        args1.max_trajectory_len = self.envs._max_episode_steps  # ex. 15 for navigation
        args2.max_trajectory_len = self.envs._max_episode_steps  # ex. 15 for navigation
        args1.max_trajectory_len *= self.args2.max_rollouts_per_task  # ex 15 * 4 for navigation
        args2.max_trajectory_len *= self.args2.max_rollouts_per_task  # ex 15 * 4 for navigation

        # calculate number of meta updates
        self.args1.num_updates = int(args1.num_frames) // args1.policy_num_steps // args1.num_processes
        self.args2.num_updates = int(args2.num_frames) // args2.policy_num_steps // args2.num_processes

        # get action / observation dimensions
        if isinstance(self.envs.action_space, gym.spaces.discrete.Discrete):
            self.args1.action_dim = 1
            self.args2.action_dim = 1
        else:
            self.args1.action_dim = self.envs.action_space.shape[0]
            self.args2.action_dim = self.envs.action_space.shape[0]
        self.args1.obs_dim = self.envs.observation_space.shape[0]
        self.args1.num_states = self.envs.num_states if str.startswith(self.args1.env_name, 'Grid') else None
        self.args1.act_space = self.envs.action_space
        self.args2.obs_dim = self.envs.observation_space.shape[0]
        self.args2.num_states = self.envs.num_states if str.startswith(self.args2.env_name, 'Grid') else None
        self.args2.act_space = self.envs.action_space

        self.vae = VaribadVAE(self.args1, self.logger, lambda: self.iter_idx) #latent dynamics network
        self.vae_pol = VaribadVAE(self.args2, self.logger, lambda: self.iter_idx) #policy network

        self.initialise_policy()

    def initialise_policy(self):

        # initialise rollout storage for the policy
        self.policy_storage = OnlineStorage(self.args2,
                                            self.args2.policy_num_steps,
                                            self.args2.num_processes,
                                            self.args2.obs_dim,
                                            self.args2.act_space,
                                            hidden_size=self.args2.aggregator_hidden_size,
                                            latent_dim=self.args2.latent_dim,
                                            normalise_observations=self.args2.norm_obs_for_policy,
                                            normalise_rewards=self.args2.norm_rew_for_policy,
                                            )

        # initialise policy network
        input_dim = self.args2.obs_dim * int(self.args2.condition_policy_on_state)
        input_dim += (1 + int(not self.args2.sample_embeddings)) * self.args2.latent_dim

        if hasattr(self.envs.action_space, 'low'):
            action_low = self.envs.action_space.low
            action_high = self.envs.action_space.high
        else:
            action_low = action_high = None

        policy_net = Policy(
            state_dim=input_dim,
            action_space=self.args2.act_space,
            init_std=self.args2.policy_init_std,
            hidden_layers=self.args2.policy_layers,
            activation_function=self.args2.policy_activation_function,
            normalise_actions=self.args2.normalise_actions,
            action_low=action_low,
            action_high=action_high,
        ).to(device)

        # initialise policy trainer
        if self.args2.policy == 'a2c':
            self.policy = A2C(
                policy_net,
                self.args2.policy_value_loss_coef,
                self.args2.policy_entropy_coef,
                optimiser_vae=self.vae_pol.optimiser_vae,
                lr=self.args2.lr_policy,
                eps=self.args2.policy_eps,
                alpha=self.args2.a2c_alpha,
            )

        elif self.args2.policy == 'ppo':
            self.policy = PPO(
                policy_net,
                self.args2.policy_value_loss_coef,
                self.args2.policy_entropy_coef,
                optimiser_vae=self.vae_pol.optimiser_vae,
                lr=self.args2.lr_policy,
                eps=self.args2.policy_eps,
                ppo_epoch=self.args2.ppo_num_epochs,
                num_mini_batch=self.args2.ppo_num_minibatch,
                use_huber_loss=self.args2.ppo_use_huberloss,
                use_clipped_value_loss=self.args2.ppo_use_clipped_value_loss,
                clip_param=self.args2.ppo_clip_param,
            )
        else:
            raise NotImplementedError

    def train(self):
        """
        Given some stream of environments and a logger (tensorboard),
        (meta-)trains the policy.
        """
        start_time = time.time()

        # reset environments
        (prev_obs_raw, prev_obs_normalised) = self.envs.reset()
        prev_obs_raw = prev_obs_raw.to(device)
        prev_obs_normalised = prev_obs_normalised.to(device)

        # insert initial observation / embeddings to rollout storage
        self.policy_storage.prev_obs_raw[0].copy_(prev_obs_raw)
        self.policy_storage.prev_obs_normalised[0].copy_(prev_obs_normalised)
        self.policy_storage.to(device)

        vae_is_pretrained = False
        for self.iter_idx in range(self.args2.num_updates):

            # First, re-compute the hidden states given the current rollouts (since the VAE might've changed)
            # compute latent embedding (will return prior if current trajectory is empty)
            with torch.no_grad():
                latent_sample, latent_mean, latent_logvar, hidden_state = self.encode_running_trajectory(vae_pol=False)
                latent_sample_pol, latent_mean_pol, latent_logvar_pol, hidden_state_pol = self.encode_running_trajectory(vae_pol=True)

            # check if we flushed the policy storage
            assert len(self.policy_storage.latent_mean) == 0

            # add this initial hidden state to the policy storage
            self.policy_storage.hidden_states[0].copy_(hidden_state_pol)
            self.policy_storage.latent_samples.append(latent_sample_pol.clone())
            self.policy_storage.latent_mean.append(latent_mean_pol.clone())
            self.policy_storage.latent_logvar.append(latent_logvar_pol.clone())

            mixture_list = np.zeros(self.args2.num_processes)
            mixture_number = self.args1.mixture_number
            extrapolation_beta = self.args1.extrapolation_beta
            normal_number = self.args2.num_processes - mixture_number

            mixture_index = np.sort(np.random.choice(self.args2.num_processes, mixture_number, replace=False))
            normal_index = np.setdiff1d(np.arange(0, self.args2.num_processes), mixture_index)
            mixture_list[mixture_index] = 1

            # sampling dirichlet weights alphas for each dimension and each worker
            # iid weight sampling for each dimension of the latent
            with torch.no_grad():
                param_dir = np.ones(normal_number)
                alphas = torch.from_numpy(
                    extrapolation_beta * np.random.dirichlet(param_dir, size=self.args1.latent_dim * mixture_number) - (extrapolation_beta-1.0) / normal_number).float().to(device)

            for step in range(self.args2.policy_num_steps):  # ex gridworld 60
                # sample actions from policy
                with torch.no_grad():
                    value, action, action_log_prob = utl.select_action(
                        args=self.args2,
                        policy=self.policy,
                        obs=prev_obs_normalised if self.args2.norm_obs_for_policy else prev_obs_raw,
                        deterministic=False,
                        latent_sample=latent_sample_pol,
                        latent_mean=latent_mean_pol,
                        latent_logvar=latent_logvar_pol,
                    )
                # observe reward and next obs
                (next_obs_raw, next_obs_normalised), (rew_raw, rew_normalised), done, infos = utl.env_step(self.envs,
                                                                                                           action)

                tasks = torch.FloatTensor([info['task'] for info in infos]).to(device)
                done = torch.from_numpy(np.array(done, dtype=int)).to(device).float().view((-1, 1))

                # create mask for episode ends
                masks_done = torch.FloatTensor([[0.0] if done_ else [1.0] for done_ in done]).to(device)
                # bad_mask is true if episode ended because time limit was reached
                bad_masks = torch.FloatTensor(
                    [[0.0] if 'bad_transition' in info.keys() else [1.0] for info in infos]).to(device)

                with torch.no_grad():
                    #generating the latent dynamics mixture for mixture workers
                    for i in range(mixture_number):
                        for j in range(self.args1.latent_dim):
                            latent_sample[mixture_index[i], j] = torch.dot(alphas[self.args1.latent_dim * i + j],
                                                                           latent_sample[normal_index][:, j])

                    #generating mixture rewards from the learned decoder
                    _, rew_pred = self.vae.compute_rew_reconstruction_loss(latent_sample[mixture_index],
                                                                           prev_obs_raw[mixture_index],
                                                                           next_obs_raw[mixture_index],
                                                                           action[mixture_index],
                                                                           rew_raw[mixture_index],
                                                                           return_predictions=True)

                    #For gridworld, the reward prediction is given as probability (0 to 1.0), we convert it as a reward (-0.1 to 1.0)
                    if self.args2.env_name == 'GridNavi-v0': #gridworld
                        rew_raw[mixture_index] = 1.1*rew_pred.clone()-0.1
                    #For MuJoCo, the reward is in real value
                    else:
                        rew_raw[mixture_index] = rew_pred.clone()

                # compute next embedding (for next loop and/or value prediction bootstrap)
                latent_sample, latent_mean, latent_logvar, hidden_state = utl.update_encoding(encoder=self.vae.encoder,
                                                                                              next_obs=next_obs_raw,
                                                                                              action=action,
                                                                                              reward=rew_raw,
                                                                                              done=done,
                                                                                              hidden_state=hidden_state)

                latent_sample_pol, latent_mean_pol, latent_logvar_pol, hidden_state_pol = utl.update_encoding(encoder=self.vae_pol.encoder,
                                                                                              next_obs=next_obs_raw,
                                                                                              action=action,
                                                                                              reward=rew_raw,
                                                                                              done=done,
                                                                                              hidden_state=hidden_state_pol)

                if not (self.args1.disable_decoder and self.args1.disable_stochasticity_in_latent):
                    self.vae.rollout_storage.insert(prev_obs_raw.clone(),
                                                    action.detach().clone(),
                                                    next_obs_raw.clone(),
                                                    rew_raw.clone(),
                                                    done.clone(),
                                                    tasks.clone(),
                                                    mixture_list) # we do not train the vae with mixture tasks

                self.policy_storage.next_obs_raw[step] = next_obs_raw.clone()
                self.policy_storage.next_obs_normalised[step] = next_obs_normalised.clone()

                for i in np.argwhere(done.cpu().detach().flatten()).flatten():  # e.x i for 16 workers
                    [next_obs_raw[i], next_obs_normalised[i]] = self.envs.reset(index=i)

                    # get a new posterior sample
                    if not self.args2.sample_embeddings:
                        latent_sample[i] = latent_sample[i]
                        latent_sample_pol[i] = latent_sample_pol[i]

                self.policy_storage.insert(
                    obs_raw=next_obs_raw,
                    obs_normalised=next_obs_normalised,
                    actions=action,
                    action_log_probs=action_log_prob,
                    rewards_raw=rew_raw,
                    rewards_normalised=rew_normalised,
                    value_preds=value,
                    masks=masks_done,
                    bad_masks=bad_masks,
                    done=done,
                    hidden_states=hidden_state_pol.squeeze(0).detach(),
                    latent_sample=latent_sample_pol.detach(),
                    latent_mean=latent_mean_pol.detach(),
                    latent_logvar=latent_logvar_pol.detach(),
                )
                # prev_latent_logvar = curr_latent_logvar

                prev_obs_normalised = next_obs_normalised
                prev_obs_raw = next_obs_raw

                self.frames += self.args1.num_processes

            # --- UPDATE ---

            if self.args1.precollect_len <= self.frames:
                # check if we are pre-training the VAE
                if self.args1.pretrain_len > 0 and not vae_is_pretrained:
                    for _ in range(self.args1.pretrain_len):
                        self.vae.compute_vae_loss(update=True)
                    vae_is_pretrained = True

                # otherwise do the normal update (policy + vae)
                else:

                    #update the policy part
                    train_stats = self.update(
                        obs=prev_obs_normalised if self.args2.norm_obs_for_policy else prev_obs_raw,
                        latent_sample=latent_sample_pol, latent_mean=latent_mean_pol, latent_logvar=latent_logvar_pol)

                    #update the vae part
                    for _ in range(self.args1.num_vae_updates):
                        self.vae.compute_vae_loss(update=True)

                    # log
                    run_stats = [action, action_log_prob, value]
                    if train_stats is not None:
                        self.log(run_stats, train_stats, start_time)

            # clean up after update
            self.policy_storage.after_update()

    def encode_running_trajectory(self, vae_pol=False):
        """
        (Re-)Encodes (for each process) the entire current trajectory.
        Returns sample/mean/logvar and hidden state (if applicable) for the current timestep.
        :return:
        """

        # for each process, get the current batch (zero-padded obs/act/rew + length indicators)
        prev_obs, next_obs, act, rew, lens = self.vae.rollout_storage.get_running_batch()

        # get embedding - will return (1+sequence_len) * batch * input_size -- includes the prior!
        if vae_pol:
            all_latent_samples, all_latent_means, all_latent_logvars, all_hidden_states = self.vae_pol.encoder(actions=act,
                                                                                                           states=next_obs,
                                                                                                           rewards=rew,
                                                                                                           hidden_state=None,
                                                                                                           return_prior=True)
        else:
            all_latent_samples, all_latent_means, all_latent_logvars, all_hidden_states = self.vae.encoder(actions=act,
                                                                                                           states=next_obs,
                                                                                                           rewards=rew,
                                                                                                           hidden_state=None,
                                                                                                           return_prior=True)

        # get the embedding / hidden state of the current time step (need to do this since we zero-padded)
        latent_sample = (torch.stack([all_latent_samples[lens[i]][i] for i in range(len(lens))])).detach().to(device)
        latent_mean = (torch.stack([all_latent_means[lens[i]][i] for i in range(len(lens))])).detach().to(device)
        latent_logvar = (torch.stack([all_latent_logvars[lens[i]][i] for i in range(len(lens))])).detach().to(device)
        hidden_state = (torch.stack([all_hidden_states[lens[i]][i] for i in range(len(lens))])).detach().to(device)

        return latent_sample, latent_mean, latent_logvar, hidden_state

    def get_value(self, obs, latent_sample, latent_mean, latent_logvar):
        obs = utl.get_augmented_obs(self.args2, obs, latent_sample, latent_mean, latent_logvar)
        return self.policy.actor_critic.get_value(obs).detach()

    def update(self, obs, latent_sample, latent_mean, latent_logvar):
        """
        Meta-update.
        Here the policy is updated for good average performance across tasks.
        :return:
        """
        # update policy (if we are not pre-training, have enough data in the vae buffer, and are not at iteration 0)
        if self.iter_idx >= self.args2.pretrain_len and self.iter_idx > 0:

            # bootstrap next value prediction
            with torch.no_grad():
                next_value = self.get_value(obs=obs,
                                            latent_sample=latent_sample,
                                            latent_mean=latent_mean,
                                            latent_logvar=latent_logvar)

            # compute returns for current rollouts
            self.policy_storage.compute_returns(next_value, self.args2.policy_use_gae, self.args2.policy_gamma,
                                                self.args2.policy_tau,
                                                use_proper_time_limits=self.args2.use_proper_time_limits)

            # update agent (this will also call the VAE update!)
            policy_train_stats = self.policy.update(
                args=self.args2,
                policy_storage=self.policy_storage,
                encoder=self.vae_pol.encoder,
                rlloss_through_encoder=self.args2.rlloss_through_encoder,
                compute_vae_loss=self.vae_pol.compute_vae_loss)
        else:
            policy_train_stats = 0, 0, 0, 0

            # pre-train the VAE
            if self.iter_idx < self.args2.pretrain_len:
                self.vae.compute_vae_loss(update=True)

        return policy_train_stats, None

    def log(self, run_stats, train_stats, start_time):
        train_stats, meta_train_stats = train_stats

        # --- visualise behaviour of policy ---

        if self.iter_idx % self.args2.vis_interval == 0:
            obs_rms = self.envs.venv.obs_rms if self.args2.norm_obs_for_policy else None
            ret_rms = self.envs.venv.ret_rms if self.args2.norm_rew_for_policy else None

            os.makedirs('{}/{}'.format(self.logger.full_output_folder, self.iter_idx))

            if self.args2.env_name == 'GridNavi-v0': #gridworld
                ret_list = []
                for task_num in range(self.eval_task_num):
                    ret = utl_eval.visualise_behaviour(args=self.args1,
                                                       policy=self.policy,
                                                       image_folder=self.logger.full_output_folder,
                                                       iter_idx=self.iter_idx,
                                                       obs_rms=obs_rms,
                                                       ret_rms=ret_rms,
                                                       encoder=self.vae_pol.encoder,
                                                       reward_decoder=self.vae.reward_decoder,
                                                       state_decoder=self.vae.state_decoder,
                                                       task_decoder=self.vae.task_decoder,
                                                       compute_rew_reconstruction_loss=self.vae.compute_rew_reconstruction_loss,
                                                       compute_state_reconstruction_loss=self.vae.compute_state_reconstruction_loss,
                                                       compute_task_reconstruction_loss=self.vae.compute_task_reconstruction_loss,
                                                       compute_kl_loss=self.vae.compute_kl_loss,
                                                       task_num=task_num,
                                                       encoder_vae = self.vae.encoder,
                                                       args_pol = self.args2
                                                       )
                    ret_list.append(ret)

                min_ret = min(ret_list)
                max_ret = max(ret_list)
                mean_ret = sum(ret_list) / self.eval_task_num
                print('eval min/max/mean return: ', min_ret, max_ret, mean_ret)
                print('mixture number:', self.args1.mixture_number)
                ret_list.insert(0, 0)
                ret_list.insert(0, 0)
                ret_list.insert(7, 0)
                ret_list.insert(7, 0)
                #Printing the sum of returns for 4 rollouts for each task
                print(np.around(np.rot90(np.reshape(ret_list, (7, 7))), 2))

            else: #mujoco
                ret_list = []

                #since mujoco test is stochastic due to random start, you may wish to repeat the evaluation multiple times
                if (self.iter_idx + 1) % (10 * self.args2.vis_interval) == 0:
                    trial_num = 1
                else:
                    trial_num = 1
                for trial_index in range(trial_num):
                    os.makedirs('{}/{}/{}'.format(self.logger.full_output_folder, self.iter_idx, trial_index))

                    ret_list_trial = []
                    for task_num in range(self.eval_task_num):
                        ret = utl_eval.visualise_behaviour(args=self.args1,
                                                           policy=self.policy,
                                                           image_folder=self.logger.full_output_folder,
                                                           iter_idx=self.iter_idx,
                                                           obs_rms=obs_rms,
                                                           ret_rms=ret_rms,
                                                           encoder=self.vae_pol.encoder,
                                                           reward_decoder=self.vae.reward_decoder,
                                                           state_decoder=self.vae.state_decoder,
                                                           task_decoder=self.vae.task_decoder,
                                                           compute_rew_reconstruction_loss=self.vae.compute_rew_reconstruction_loss,
                                                           compute_state_reconstruction_loss=self.vae.compute_state_reconstruction_loss,
                                                           compute_task_reconstruction_loss=self.vae.compute_task_reconstruction_loss,
                                                           compute_kl_loss=self.vae.compute_kl_loss,
                                                           task_num=task_num,
                                                           encoder_vae=self.vae.encoder,
                                                           trial_num=trial_index,
                                                           args_pol=self.args2,
                                                           )
                        ret_list_trial.append(ret/self.args2.max_rollouts_per_task)
                    ret_list.append(ret_list_trial)

                std_list = np.std(ret_list, axis=0)
                ret_list = np.mean(ret_list, axis=0)

                for task_num in range(self.eval_task_num):
                    print("task num", task_num, "test return:", ret_list[task_num], "std:", std_list[task_num])

                if self.args2.env_name == 'AntDir-v0':
                    print("train task mean", np.mean(ret_list[:4]))
                    print("test task mean", np.mean(ret_list[4:8]))
                if self.args2.env_name == 'AntGoal-v0':
                    print("train task mean", np.mean(ret_list[:4]), np.mean(ret_list[8:12]))
                    print("test task mean", np.mean(ret_list[4:8]))
                elif self.args2.env_name == 'HalfCheetahVel-v0':
                    print("train task mean", np.mean(ret_list[:2]))
                    print("test task mean", np.mean(ret_list[2:7]))

                print('mixture number:', self.args1.mixture_number)
        # --- evaluate policy ----

        if self.iter_idx % self.args2.eval_interval == 0:

            obs_rms = self.envs.venv.obs_rms if self.args2.norm_obs_for_policy else None
            ret_rms = self.envs.venv.ret_rms if self.args2.norm_rew_for_policy else None

            returns_per_episode = utl_eval.evaluate(args=self.args2,
                                                    policy=self.policy,
                                                    obs_rms=obs_rms,
                                                    ret_rms=ret_rms,
                                                    encoder=self.vae_pol.encoder,
                                                    iter_idx=self.iter_idx,
                                                    )

            # log the return avg/std across tasks (=processes)
            returns_avg = returns_per_episode.mean(dim=0)
            returns_std = returns_per_episode.std(dim=0)
            for k in range(len(returns_avg)):
                self.logger.add('return_avg_per_iter/episode_{}'.format(k + 1), returns_avg[k], self.iter_idx)
                self.logger.add('return_avg_per_frame/episode_{}'.format(k + 1), returns_avg[k], self.frames)
                self.logger.add('return_std_per_iter/episode_{}'.format(k + 1), returns_std[k], self.iter_idx)
                self.logger.add('return_std_per_frame/episode_{}'.format(k + 1), returns_std[k], self.frames)

            print("Updates {}, num timesteps {}, FPS {}, {} \n Mean return (train): {:.5f} \n".
                  format(self.iter_idx, self.frames, int(self.frames / (time.time() - start_time)),
                         self.vae.rollout_storage.prev_obs.shape, returns_avg[-1].item()))

        # --- save models ---

        if self.iter_idx % self.args2.save_interval == 0:
            save_path = os.path.join(self.logger.full_output_folder, 'models')
            if not os.path.exists(save_path):
                os.mkdir(save_path)
            torch.save(self.policy.actor_critic, os.path.join(save_path, "policy{0}.pt".format(self.iter_idx)))
            torch.save(self.vae.encoder, os.path.join(save_path, "encoder{0}.pt".format(self.iter_idx)))
            if self.vae.state_decoder is not None:
                torch.save(self.vae.state_decoder, os.path.join(save_path, "state_decoder{0}.pt".format(self.iter_idx)))
            if self.vae.reward_decoder is not None:
                torch.save(self.vae.reward_decoder,
                           os.path.join(save_path, "reward_decoder{0}.pt".format(self.iter_idx)))
            if self.vae.task_decoder is not None:
                torch.save(self.vae.task_decoder, os.path.join(save_path, "task_decoder{0}.pt".format(self.iter_idx)))

            # save normalisation params of envs
            if self.args2.norm_rew_for_policy:
                # save rolling mean and std
                rew_rms = self.envs.venv.ret_rms
                utl.save_obj(rew_rms, save_path, "env_rew_rms{0}.pkl".format(self.iter_idx))
            if self.args2.norm_obs_for_policy:
                obs_rms = self.envs.venv.obs_rms
                utl.save_obj(obs_rms, save_path, "env_obs_rms{0}.pkl".format(self.iter_idx))

            # --- log some other things ---

        if self.iter_idx % self.args2.log_interval == 0:

            self.logger.add('policy_losses/value_loss', train_stats[0], self.iter_idx)
            self.logger.add('policy_losses/action_loss', train_stats[1], self.iter_idx)
            self.logger.add('policy_losses/dist_entropy', train_stats[2], self.iter_idx)
            self.logger.add('policy_losses/sum', train_stats[3], self.iter_idx)

            self.logger.add('policy/action', run_stats[0][0].float().mean(), self.iter_idx)
            if hasattr(self.policy.actor_critic, 'logstd'):
                self.logger.add('policy/action_logstd', self.policy.actor_critic.dist.logstd.mean(), self.iter_idx)
            self.logger.add('policy/action_logprob', run_stats[1].mean(), self.iter_idx)
            self.logger.add('policy/value', run_stats[2].mean(), self.iter_idx)

            self.logger.add('encoder/latent_mean', torch.cat(self.policy_storage.latent_mean).mean(), self.iter_idx)
            self.logger.add('encoder/latent_logvar', torch.cat(self.policy_storage.latent_logvar).mean(), self.iter_idx)

            # log the average weights and gradients of all models (where applicable)
            for [model, name] in [
                [self.policy.actor_critic, 'policy'],
                [self.vae.encoder, 'encoder'],
                [self.vae.reward_decoder, 'reward_decoder'],
                [self.vae.state_decoder, 'state_transition_decoder'],
                [self.vae.task_decoder, 'task_decoder']
            ]:
                # if model is not None:
                if model is not None:
                    param_list = list(model.parameters())
                    param_mean = np.mean([param_list[i].data.cpu().numpy().mean() for i in range(len(param_list))])
                    self.logger.add('weights/{}'.format(name), param_mean, self.iter_idx)
                    if name == 'policy':
                        self.logger.add('weights/policy_std', param_list[0].data.mean(), self.iter_idx)
                    if param_list[0].grad is not None:
                        param_grad_list = []
                        for i in range(len(param_list)):
                            if param_list[i].grad is not None:
                                param_grad_list.append(param_list[i].grad.cpu().numpy().mean())
                        param_grad_mean = np.mean(param_grad_list)
                        self.logger.add('gradients/{}'.format(name), param_grad_mean, self.iter_idx)