Curiosity/DPI/train.py

import os
import gc 
import copy
import tqdm
import wandb
import random
import argparse
import numpy as np

import utils
from utils import ReplayBuffer, FreezeParameters, make_env, preprocess_obs, soft_update_params, save_image
from models import ObservationEncoder, ObservationDecoder, TransitionModel, Actor, ValueModel, RewardModel, ProjectionHead, ContrastiveHead, CLUBSample
from logger import Logger
from video import VideoRecorder
from dmc2gym.wrappers import set_global_var

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as T
from torch.utils.tensorboard import SummaryWriter

#from agent.baseline_agent import BaselineAgent
#from agent.bisim_agent import BisimAgent
#from agent.deepmdp_agent import DeepMDPAgent
#from agents.navigation.carla_env import CarlaEnv


def parse_args():
    parser = argparse.ArgumentParser()
    # environment
    parser.add_argument('--domain_name', default='cheetah')
    parser.add_argument('--version', default=1, type=int)
    parser.add_argument('--task_name', default='run')
    parser.add_argument('--image_size', default=84, type=int)
    parser.add_argument('--channels', default=3, type=int)
    parser.add_argument('--action_repeat', default=2, type=int)
    parser.add_argument('--frame_stack', default=3, type=int)
    parser.add_argument('--collection_interval', default=100, type=int)
    parser.add_argument('--resource_files', type=str)
    parser.add_argument('--eval_resource_files', type=str)
    parser.add_argument('--img_source', default=None, type=str, choices=['color', 'noise', 'images', 'video', 'none'])
    parser.add_argument('--total_frames', default=1000, type=int)  # 10000
    parser.add_argument('--high_noise', action='store_true')
    # replay buffer
    parser.add_argument('--replay_buffer_capacity', default=50000, type=int)   #50000
    parser.add_argument('--episode_length', default=21, type=int)
    # train
    parser.add_argument('--agent', default='dpi', type=str, choices=['baseline', 'bisim', 'deepmdp', 'db', 'dpi', 'rpc'])
    parser.add_argument('--init_steps', default=10000, type=int)
    parser.add_argument('--num_train_steps', default=100000, type=int)
    parser.add_argument('--batch_size', default=128, type=int)  #512
    parser.add_argument('--state_size', default=30, type=int)
    parser.add_argument('--hidden_size', default=256, type=int)
    parser.add_argument('--history_size', default=128, type=int)
    parser.add_argument('--episode_collection', default=5, type=int)
    parser.add_argument('--episodes_buffer', default=20, type=int)
    parser.add_argument('--num-units', type=int, default=50, help='num hidden units for reward/value/discount models')
    parser.add_argument('--load_encoder', default=None, type=str)
    parser.add_argument('--imagine_horizon', default=10, type=str)
    parser.add_argument('--grad_clip_norm', type=float, default=100.0, help='Gradient clipping norm')
    # eval
    parser.add_argument('--eval_freq', default=10, type=int)  # TODO: master had 10000
    parser.add_argument('--num_eval_episodes', default=20, type=int)
    parser.add_argument('--evaluation_interval', default=10000, type=int)  # TODO: master had 10000
    # value
    parser.add_argument('--value_lr', default=8e-6, type=float)
    parser.add_argument('--value_beta', default=0.9, type=float)
    parser.add_argument('--value_tau', default=0.005, type=float)
    parser.add_argument('--value_target_update_freq', default=100, type=int)
    parser.add_argument('--td_lambda', default=0.95, type=int)
    # actor
    parser.add_argument('--actor_lr', default=8e-6, type=float)
    parser.add_argument('--actor_beta', default=0.9, type=float)
    parser.add_argument('--actor_log_std_min', default=-10, type=float)
    parser.add_argument('--actor_log_std_max', default=2, type=float)
    parser.add_argument('--actor_update_freq', default=2, type=int)
    # world/encoder/decoder
    parser.add_argument('--encoder_type', default='pixel', type=str, choices=['pixel', 'pixelCarla096', 'pixelCarla098', 'identity'])
    parser.add_argument('--world_model_lr', default=1e-5, type=float)
    parser.add_argument('--encoder_tau', default=0.005, type=float)
    parser.add_argument('--decoder_type', default='pixel', type=str, choices=['pixel', 'identity', 'contrastive', 'reward', 'inverse', 'reconstruction'])
    parser.add_argument('--num_layers', default=4, type=int)
    parser.add_argument('--num_filters', default=32, type=int)
    parser.add_argument('--aug', action='store_true')
    # sac
    parser.add_argument('--discount', default=0.99, type=float)
    # misc
    parser.add_argument('--seed', default=1, type=int)
    parser.add_argument('--logging_freq', default=100, type=int)
    parser.add_argument('--saving_interval', default=2500, type=int)
    parser.add_argument('--work_dir', default='.', type=str)
    parser.add_argument('--save_tb', default=False, action='store_true')
    parser.add_argument('--save_model', default=False, action='store_true')
    parser.add_argument('--save_buffer', default=False, action='store_true')
    parser.add_argument('--save_video', default=False, action='store_true')
    parser.add_argument('--transition_model_type', default='', type=str, choices=['', 'deterministic', 'probabilistic', 'ensemble'])
    parser.add_argument('--render', default=False, action='store_true')
    args = parser.parse_args()
    return args







class DPI:
    def __init__(self, args):
        # wandb config
        #run = wandb.init(project="dpi")
    
        self.args = args

        # set environment noise
        set_global_var(self.args.high_noise)

        # environment setup
        self.env = make_env(self.args)
        #self.args.seed = np.random.randint(0, 1000)
        self.env.seed(self.args.seed)
        self.global_episodes = 0

        # noiseless environment setup
        self.args.version = 2   # env_id changes to v2
        self.args.img_source = None # no image noise
        self.args.resource_files = None

        # stack several consecutive frames together
        if self.args.encoder_type.startswith('pixel'):
            self.env = utils.FrameStack(self.env, k=self.args.frame_stack)
            self.env = utils.ActionRepeat(self.env, self.args.action_repeat)
            self.env = utils.NormalizeActions(self.env)

        # create replay buffer
        self.data_buffer = ReplayBuffer(size=self.args.replay_buffer_capacity, 
                                        obs_shape=(self.args.frame_stack*self.args.channels,self.args.image_size,self.args.image_size),
                                        action_size=self.env.action_space.shape[0], 
                                        seq_len=self.args.episode_length, 
                                        batch_size=args.batch_size,
                                        args=self.args)
        
        # create work directory
        utils.make_dir(self.args.work_dir)
        self.video_dir = utils.make_dir(os.path.join(self.args.work_dir, 'video'))
        self.model_dir = utils.make_dir(os.path.join(self.args.work_dir, 'model'))
        self.buffer_dir = utils.make_dir(os.path.join(self.args.work_dir, 'buffer'))

        # create models
        self.build_models(use_saved=False, saved_model_dir=self.model_dir)

    def build_models(self, use_saved, saved_model_dir=None):
        # World Models
        self.obs_encoder = ObservationEncoder(
                            obs_shape=(self.args.frame_stack*self.args.channels,self.args.image_size,self.args.image_size), # (9,84,84)
                            state_size=self.args.state_size # 128
                            ).to(device)
        
        self.obs_encoder_momentum = ObservationEncoder(
                            obs_shape=(self.args.frame_stack*self.args.channels,self.args.image_size,self.args.image_size), # (9,84,84)
                            state_size=self.args.state_size # 128
                            ).to(device)

        self.obs_decoder = ObservationDecoder(
                            state_size=self.args.state_size, # 128
                            output_shape=(self.args.channels*self.args.channels,self.args.image_size,self.args.image_size) # (3,84,84)
                            ).to(device)

        self.transition_model = TransitionModel(
                            state_size=self.args.state_size, # 128
                            hidden_size=self.args.hidden_size, # 256
                            action_size=self.env.action_space.shape[0], # 6
                            history_size=self.args.history_size, # 128
                            ).to(device)
        
        # Actor Model
        self.actor_model = Actor(
                            state_size=self.args.state_size, # 128
                            hidden_size=self.args.hidden_size, # 256, 
                            action_size=self.env.action_space.shape[0], # 6
                            ).to(device)
        self.actor_model.apply(self.init_weights)


        # Value Models
        self.value_model = ValueModel(
                            state_size=self.args.state_size, # 128
                            hidden_size=self.args.hidden_size, # 256
                            ).to(device)
        
        self.target_value_model = ValueModel(
                            state_size=self.args.state_size, # 128
                            hidden_size=self.args.hidden_size, # 256
                            ).to(device)

        self.reward_model = RewardModel(
                            state_size=self.args.state_size, # 128
                            hidden_size=self.args.hidden_size, # 256
                            ).to(device)
        
        # Contrastive Models
        self.prjoection_head = ProjectionHead(
                            state_size=self.args.state_size, # 128
                            action_size=self.env.action_space.shape[0], # 6
                            hidden_size=self.args.hidden_size, # 256
                            ).to(device)
        
        self.prjoection_head_momentum = ProjectionHead(
                            state_size=self.args.state_size, # 128
                            action_size=self.env.action_space.shape[0], # 6
                            hidden_size=self.args.hidden_size, # 256
                            ).to(device)
        
        self.contrastive_head = ContrastiveHead(
                            hidden_size=self.args.hidden_size, # 256
                            ).to(device)
        
        self.club_sample = CLUBSample(
                            x_dim=self.args.state_size, # 128
                            y_dim=self.args.state_size, # 128
                            hidden_size=self.args.hidden_size, # 256
                            ).to(device)


        # model parameters
        self.world_model_parameters = list(self.obs_encoder.parameters()) + list(self.prjoection_head.parameters()) + \
                                list(self.transition_model.parameters()) + list(self.obs_decoder.parameters()) + \
                                list(self.reward_model.parameters()) + list(self.club_sample.parameters())
        self.past_transition_parameters = self.transition_model.parameters()

        # optimizers
        self.world_model_opt = torch.optim.Adam(self.world_model_parameters, self.args.world_model_lr)
        self.value_opt = torch.optim.Adam(self.value_model.parameters(), self.args.value_lr)
        self.actor_opt = torch.optim.Adam(self.actor_model.parameters(), self.args.actor_lr)
        #self.reward_opt = torch.optim.Adam(self.reward_model.parameters(), 1e-5)
        #self.decoder_opt = torch.optim.Adam(self.obs_decoder.parameters(), 1e-4)

        # Create Modules
        self.world_model_modules = [self.obs_encoder, self.prjoection_head, self.transition_model, self.obs_decoder, self.reward_model, self.club_sample]
        self.value_modules = [self.value_model]
        self.actor_modules = [self.actor_model]
        #self.reward_modules = [self.reward_model]
        #self.decoder_modules = [self.obs_decoder]

        if use_saved:
            self._use_saved_models(saved_model_dir)

    def _use_saved_models(self, saved_model_dir):
        self.obs_encoder.load_state_dict(torch.load(os.path.join(saved_model_dir, 'obs_encoder.pt')))
        self.obs_decoder.load_state_dict(torch.load(os.path.join(saved_model_dir, 'obs_decoder.pt')))
        self.transition_model.load_state_dict(torch.load(os.path.join(saved_model_dir, 'transition_model.pt')))

    def collect_sequences(self, episodes,  random=True, actor_model=None, encoder_model=None):
        obs = self.env.reset()
        done = False

        all_rews = []
        for episode_count in tqdm.tqdm(range(episodes), desc='Collecting episodes'):
            self.global_episodes += 1
            epi_reward = 0
            while not done:
                if random:
                    action = self.env.action_space.sample()
                else:
                    with torch.no_grad():
                        obs = torch.tensor(obs.copy(), dtype=torch.float32).unsqueeze(0)
                        obs_processed = preprocess_obs(obs).to(device)
                        state = self.obs_encoder(obs_processed)["distribution"].sample()
                        action = self.actor_model(state).cpu().numpy().squeeze() 
                        #action = self.env.action_space.sample()

                next_obs, rew, done, _ = self.env.step(action)
                self.data_buffer.add(obs, action, next_obs, rew, done, self.global_episodes)
                obs = next_obs  
                epi_reward += rew

            obs = self.env.reset()
            done=False
            all_rews.append(epi_reward)
        return all_rews

    def train(self, step, total_steps):
        counter = 0
        import matplotlib.pyplot as plt
        fig, ax = plt.subplots()
        while step < total_steps:
            
            # collect experience
            if step !=0:
                encoder = self.obs_encoder
                actor = self.actor_model
                all_rews = self.collect_sequences(self.args.episode_collection, random=False, actor_model=actor, encoder_model=encoder)
            else:
                all_rews = self.collect_sequences(self.args.episodes_buffer, random=True)

            # collect sequences
            non_zero_indices = np.nonzero(self.data_buffer.episode_count)[0]
            current_obs = self.data_buffer.observations[non_zero_indices]
            next_obs = self.data_buffer.next_observations[non_zero_indices]
            actions_raw = self.data_buffer.actions[non_zero_indices]
            rewards = self.data_buffer.rewards[non_zero_indices]
            self.terms = np.where(self.data_buffer.terminals[non_zero_indices]!=0)[0]
            
            # Group by episodes
            current_obs = self.grouped_arrays(current_obs)
            next_obs = self.grouped_arrays(next_obs)
            actions_raw = self.grouped_arrays(actions_raw)
            rewards_ = self.grouped_arrays(rewards)

            # Train encoder
            if step == 0:
                step += 1
            
            update_steps = 1 if step > 1 else 1
            #for _ in range(self.args.collection_interval // self.args.episode_length+1):
            for _ in range(update_steps):
                counter += 1

                # Select random chunks of episodes
                if current_obs.shape[0] < self.args.batch_size:
                    random_episode_number = np.random.randint(0, current_obs.shape[0], self.args.batch_size)
                else:
                    random_episode_number = random.sample(range(current_obs.shape[0]), self.args.batch_size)
                
                if current_obs[0].shape[0]-self.args.episode_length < self.args.batch_size:
                    init_index = np.random.randint(0, current_obs[0].shape[0]-self.args.episode_length-2, self.args.batch_size)
                else:
                    init_index = np.asarray(random.sample(range(current_obs[0].shape[0]-self.args.episode_length), self.args.batch_size))
                random.shuffle(random_episode_number)
                random.shuffle(init_index)
                last_observations = self.select_first_k(current_obs, init_index, random_episode_number)[:-1]
                current_observations = self.select_first_k(current_obs, init_index, random_episode_number)[1:]
                next_observations = self.select_first_k(next_obs, init_index, random_episode_number)[:-1]
                actions = self.select_first_k(actions_raw, init_index, random_episode_number)[:-1].to(device)
                next_actions = self.select_first_k(actions_raw, init_index, random_episode_number)[1:].to(device)
                rewards = self.select_first_k(rewards_, init_index, random_episode_number)[1:].to(device)
                
                # Preprocessing
                last_observations = preprocess_obs(last_observations).to(device)
                current_observations = preprocess_obs(current_observations).to(device)
                next_observations = preprocess_obs(next_observations).to(device)

                # Initialize transition model states
                self.transition_model.init_states(self.args.batch_size, device) # (N,128) 
                self.history = self.transition_model.prev_history # (N,128)

                past_world_model_loss = 0
                past_action_loss = 0
                past_value_loss = 0
                for i in range(self.args.episode_length-1):
                    if i > 0:
                        # Encode observations and next_observations
                        self.last_states_dict = self.get_features(last_observations[i])
                        self.current_states_dict = self.get_features(current_observations[i])
                        self.next_states_dict = self.get_features(next_observations[i], momentum=True)
                        self.action = actions[i] # (N,6)
                        self.next_action = next_actions[i] # (N,6)
                        history = self.transition_model.prev_history
                        
                        # Encode negative observations fro upper bound loss
                        idx = torch.randperm(current_observations[i].shape[0]) # random permutation on batch
                        random_time_index = torch.randint(0, current_observations.shape[0]-2, (1,)).item() # random time index
                        negative_current_observations = current_observations[random_time_index][idx]
                        self.negative_current_states_dict = self.get_features(negative_current_observations)    

                        # Predict current state from past state with transition model
                        last_states_sample = self.last_states_dict["sample"]
                        predicted_current_state_dict = self.transition_model.imagine_step(last_states_sample, self.action, self.history)
                        self.history = predicted_current_state_dict["history"]

                        # Calculate upper bound loss
                        likeli_loss, ub_loss = self._upper_bound_minimization(self.last_states_dict["sample"].detach(), 
                                                                            self.current_states_dict["sample"].detach(),
                                                                            self.negative_current_states_dict["sample"].detach(),
                                                                            predicted_current_state_dict["sample"].detach(),
                                                                            )   
                        
                        # Calculate encoder loss
                        encoder_loss = self._past_encoder_loss(self.current_states_dict, predicted_current_state_dict)

                        # decoder loss
                        horizon = np.minimum(self.args.imagine_horizon, self.args.episode_length-1-i)
                        nxt_obs = next_observations[i:i+horizon].reshape(-1,9,84,84)
                        next_states_encodings = self.get_features(nxt_obs)["sample"].view(horizon,self.args.batch_size, -1)
                        obs_dist = self.obs_decoder(next_states_encodings)
                        decoder_loss = -torch.mean(obs_dist.log_prob(next_observations[i:i+horizon]))
                        
                        # contrastive projection
                        vec_anchor = predicted_current_state_dict["sample"].detach()
                        vec_positive = self.next_states_dict["sample"].detach()
                        z_anchor = self.prjoection_head(vec_anchor, self.action)
                        z_positive = self.prjoection_head_momentum(vec_positive, next_actions[i]).detach()

                        # contrastive loss
                        logits = self.contrastive_head(z_anchor, z_positive)
                        labels = torch.arange(logits.shape[0]).long().to(device)
                        lb_loss = F.cross_entropy(logits, labels)

                        # reward loss  
                        reward_dist = self.reward_model(self.current_states_dict["sample"])
                        reward_loss = -torch.mean(reward_dist.log_prob(rewards[i]))

                        # world model loss
                        world_model_loss =  (10*encoder_loss + 10*ub_loss + 1e-1*lb_loss + reward_loss + 1e-3*decoder_loss + past_world_model_loss) * 1e-3
                        past_world_model_loss = world_model_loss.item()

                        # actor loss
                        with FreezeParameters(self.world_model_modules):
                            imagine_horizon = self.args.imagine_horizon #np.minimum(self.args.imagine_horizon, self.args.episode_length-1-i)
                            action = self.actor_model(self.current_states_dict["sample"])
                            imagined_rollout = self.transition_model.imagine_rollout(self.current_states_dict["sample"], 
                                                                                    action, self.history,
                                                                                    imagine_horizon)  

                        with FreezeParameters(self.world_model_modules + self.value_modules):
                            imag_rewards = self.reward_model(imagined_rollout["sample"]).mean
                            imag_values = self.target_value_model(imagined_rollout["sample"]).mean

                            discounts =  self.args.discount * torch.ones_like(imag_rewards).detach()
                        
                        self.returns = self._compute_lambda_return(imag_rewards[:-1], 
                                                                   imag_values[:-1], 
                                                                   discounts[:-1] ,
                                                                   self.args.td_lambda, 
                                                                   imag_values[-1])

                        discounts = torch.cat([torch.ones_like(discounts[:1]), discounts[1:-1]], 0)
                        self.discounts = torch.cumprod(discounts, 0).detach()
                        actor_loss = -torch.mean(self.discounts * self.returns) + past_action_loss
                        past_action_loss = actor_loss.item()

                        # value loss
                        with torch.no_grad():
                            value_feat = imagined_rollout["sample"][:-1].detach()
                            value_targ = self.returns.detach()

                        value_dist = self.value_model(value_feat)
                        
                        value_loss = -torch.mean(self.discounts * value_dist.log_prob(value_targ).unsqueeze(-1)) + past_value_loss
                        past_value_loss = value_loss.item()

                        # update target value
                        if step % self.args.value_target_update_freq == 0:
                            self.target_value_model = copy.deepcopy(self.value_model)
                        
                        # counter for reward
                        #count = np.arange((counter-1) * (self.args.batch_size), (counter) * (self.args.batch_size))
                        count = (counter-1) * (self.args.batch_size)
                        if step % self.args.logging_freq:
                            writer.add_scalar('World Loss/World Loss', world_model_loss.detach().item(), self.data_buffer.steps)
                            writer.add_scalar('Main Models Loss/Encoder Loss', encoder_loss.detach().item(), self.data_buffer.steps)
                            writer.add_scalar('Main Models Loss/Decoder Loss', decoder_loss, self.data_buffer.steps)
                            writer.add_scalar('Actor Critic Loss/Actor Loss', actor_loss.detach().item(), self.data_buffer.steps)
                            writer.add_scalar('Actor Critic Loss/Value Loss', value_loss.detach().item(), self.data_buffer.steps)
                            writer.add_scalar('Actor Critic Loss/Reward Loss', reward_loss.detach().item(), self.data_buffer.steps)
                            writer.add_scalar('Bound Loss/Upper Bound Loss', ub_loss.detach().item(), self.data_buffer.steps)
                            writer.add_scalar('Bound Loss/Lower Bound Loss', lb_loss.detach().item(), self.data_buffer.steps)
                        step += 1
                
                print(world_model_loss, actor_loss, value_loss)
                
                # update actor model
                self.actor_opt.zero_grad()
                actor_loss.backward()
                nn.utils.clip_grad_norm_(self.actor_model.parameters(), self.args.grad_clip_norm)
                self.actor_opt.step()

                # update world model
                self.world_model_opt.zero_grad()
                world_model_loss.backward()
                nn.utils.clip_grad_norm_(self.world_model_parameters, self.args.grad_clip_norm)
                self.world_model_opt.step()

                # update value model
                self.value_opt.zero_grad()
                value_loss.backward()
                nn.utils.clip_grad_norm_(self.value_model.parameters(), self.args.grad_clip_norm)
                self.value_opt.step()

                # update momentum encoder and projection head
                soft_update_params(self.obs_encoder, self.obs_encoder_momentum, self.args.encoder_tau)
                soft_update_params(self.prjoection_head, self.prjoection_head_momentum, self.args.encoder_tau)

            rew_len = np.arange(count, count+self.args.episode_collection) if count != 0 else np.arange(0, self.args.batch_size)
            for j in range(len(all_rews)):
                writer.add_scalar('Rewards/Rewards', all_rews[j], rew_len[j])

            print(step)
            if step % 2850 == 0 and self.data_buffer.steps!=0: #self.args.evaluation_interval == 0:
                print("Saving model")
                path =  os.path.dirname(os.path.realpath(__file__)) + "/saved_models/models.pth"
                self.save_models(path)
                self.evaluate()
                        

    def evaluate(self, eval_episodes=10):
        path = path =  os.path.dirname(os.path.realpath(__file__)) + "/saved_models/models.pth"
        self.restore_checkpoint(path)

        obs = self.env.reset()
        done = False

        #video = VideoRecorder(self.video_dir, resource_files=self.args.resource_files)
        if self.args.save_video:
            self.env.video.init(enabled=True)
        episodic_rewards = []
        for episode in range(eval_episodes):
            rewards = 0
            done = False
            while not done:
                with torch.no_grad():
                    obs = torch.tensor(obs.copy(), dtype=torch.float32).unsqueeze(0)
                    obs_processed = preprocess_obs(obs).to(device)
                    state = self.obs_encoder(obs_processed)["distribution"].sample()
                    action = self.actor_model(state).cpu().detach().numpy().squeeze()       
                        
                next_obs, rew, done, _ = self.env.step(action)
                rewards += rew

                if self.args.save_video:
                    self.env.video.record(self.env)
                    self.env.video.save('/home/vedant/Curiosity/Curiosity/DPI/log/video/learned_model.mp4')
                obs = next_obs
            obs = self.env.reset()
            episodic_rewards.append(rewards)
        print("Episodic rewards: ", episodic_rewards)
        print("Average episodic reward: ", np.mean(episodic_rewards))
    
    def init_weights(self, m):
        if isinstance(m, nn.Linear):
            torch.nn.init.xavier_uniform_(m.weight)
            m.bias.data.fill_(0.01)


    def grouped_arrays(self,array):
        indices = [0] + self.terms.tolist()

        def subarrays():
            for start, end in zip(indices[:-1], indices[1:]):
                yield array[start:end]

        try:
            subarrays = np.stack(list(subarrays()), axis=0)
        except ValueError:
            subarrays = np.asarray(list(subarrays()))

        return subarrays
    
    def select_first_k(self, array, init_index, episode_number):
        term_index = init_index + self.args.episode_length
        array = array[episode_number]

        array_list = []
        for i in range(array.shape[0]):
            array_list.append(array[i][init_index[i]:term_index[i]])
        array = np.asarray(array_list)

        if array.ndim == 5:
            transposed_array = np.transpose(array, (1, 0, 2, 3, 4))
        elif array.ndim == 4:
            transposed_array = np.transpose(array, (1, 0, 2, 3))
        elif array.ndim == 3:
            transposed_array = np.transpose(array, (1, 0, 2))
        elif array.ndim == 2:
            transposed_array = np.transpose(array, (1, 0))
        else:
            transposed_array = np.expand_dims(array, axis=0)
        return torch.tensor(transposed_array).float()

    def _upper_bound_minimization(self, last_states, current_states, negative_current_states, predicted_current_states):
        club_loss = self.club_sample(current_states, predicted_current_states, negative_current_states)
        likelihood_loss = 0
        return likelihood_loss, club_loss
    
    def _past_encoder_loss(self, curr_states_dict, predicted_curr_states_dict):
        # current state distribution
        curr_states_dist = curr_states_dict["distribution"]

        # predicted current state distribution
        predicted_curr_states_dist = predicted_curr_states_dict["distribution"]

        # KL divergence loss
        loss = torch.mean(torch.distributions.kl.kl_divergence(curr_states_dist,predicted_curr_states_dist))

        return loss
    
    def get_features(self, x, momentum=False):        
        if self.args.aug:
            crop_transform = T.RandomCrop(size=80)
            cropped_x = torch.stack([crop_transform(x[i]) for i in range(x.size(0))])
            padding = (2, 2, 2, 2)
            x = F.pad(cropped_x, padding)

        with torch.no_grad():
            if momentum:
                x = self.obs_encoder_momentum(x)
            else:
                x = self.obs_encoder(x)
        return x
    
    def _compute_lambda_return(self, rewards, values, discounts, td_lam, last_value):
        next_values = torch.cat([values[1:], last_value.unsqueeze(0)],0)  
        targets = rewards + discounts * next_values * (1-td_lam)
        rets =[]
        last_rew = last_value

        for t in range(rewards.shape[0]-1, -1, -1):
            last_rew = targets[t] + discounts[t] * td_lam *(last_rew)
            rets.append(last_rew)

        returns = torch.flip(torch.stack(rets), [0])
        return returns
    
    def save_models(self, save_path):
        torch.save(
            {'rssm' : self.transition_model.state_dict(),
            'actor': self.actor_model.state_dict(),
            'reward_model': self.reward_model.state_dict(),
            'obs_encoder': self.obs_encoder.state_dict(),
            'obs_decoder': self.obs_decoder.state_dict(),
            'actor_optimizer': self.actor_opt.state_dict(),
            'value_optimizer': self.value_opt.state_dict(),
            'world_model_optimizer': self.world_model_opt.state_dict(),}, save_path)
    
    def restore_checkpoint(self, ckpt_path):
        checkpoint = torch.load(ckpt_path)
        self.transition_model.load_state_dict(checkpoint['rssm'])
        self.actor_model.load_state_dict(checkpoint['actor'])
        self.reward_model.load_state_dict(checkpoint['reward_model'])
        self.obs_encoder.load_state_dict(checkpoint['obs_encoder'])
        self.obs_decoder.load_state_dict(checkpoint['obs_decoder'])
        self.world_model_opt.load_state_dict(checkpoint['world_model_optimizer'])
        self.actor_opt.load_state_dict(checkpoint['actor_optimizer'])
        self.value_opt.load_state_dict(checkpoint['value_optimizer'])

if __name__ == '__main__':
    args = parse_args()
    
    writer = SummaryWriter()
    

    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

    step = 0
    total_steps = 500000
    dpi = DPI(args)
    dpi.train(step,total_steps)
    dpi.evaluate()