Curiosity/DPI/train.py

import numpy as np
import torch
import argparse
import os
import gym
import time
import json
import dmc2gym

import tqdm
import wandb
import utils
from utils import ReplayBuffer, FreezeParameters, make_env, 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.nn as nn
import torch.nn.functional as F
import torchvision.transforms as T


#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=1, type=int)
    parser.add_argument('--frame_stack', default=3, 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=51, 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=10000, type=int)
    parser.add_argument('--batch_size', default=20, type=int)  #512
    parser.add_argument('--state_size', default=256, type=int)
    parser.add_argument('--hidden_size', default=128, type=int)
    parser.add_argument('--history_size', default=128, type=int)
    parser.add_argument('--num-units', type=int, default=200, help='num hidden units for reward/value/discount models')
    parser.add_argument('--load_encoder', default=None, type=str)
    parser.add_argument('--imagine_horizon', default=15, 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)
    # value
    parser.add_argument('--value_lr', default=1e-4, 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=2, type=int)
    parser.add_argument('--td_lambda', default=0.95, type=int)
    # reward
    parser.add_argument('--reward_lr', default=1e-4, type=float)
    # actor
    parser.add_argument('--actor_lr', default=1e-4, 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('--encoder_feature_dim', default=50, type=int)
    parser.add_argument('--world_model_lr', default=1e-3, type=float)
    parser.add_argument('--past_transition_lr', default=1e-3, type=float)
    parser.add_argument('--encoder_lr', default=1e-3, type=float)
    parser.add_argument('--encoder_tau', default=0.005, type=float)
    parser.add_argument('--encoder_stride', default=1, type=int)
    parser.add_argument('--decoder_type', default='pixel', type=str, choices=['pixel', 'identity', 'contrastive', 'reward', 'inverse', 'reconstruction'])
    parser.add_argument('--decoder_lr', default=1e-3, type=float)
    parser.add_argument('--decoder_update_freq', default=1, type=int)
    parser.add_argument('--decoder_weight_lambda', default=0.0, type=float)
    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)
    parser.add_argument('--init_temperature', default=0.01, type=float)
    parser.add_argument('--alpha_lr', default=1e-3, type=float)
    parser.add_argument('--alpha_beta', default=0.9, type=float)
    # misc
    parser.add_argument('--seed', default=1, type=int)
    parser.add_argument('--logging_freq', default=100, 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')
    parser.add_argument('--port', default=2000, type=int)
    parser.add_argument('--num_likelihood_updates', default=5, type=int)
    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)

        # 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
        self.env_clean = make_env(self.args)
        self.env_clean.seed(self.args.seed)

        # 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_clean = utils.FrameStack(self.env_clean, k=self.args.frame_stack)

        # 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), # (12,84,84)
                            state_size=self.args.state_size # 128
                            )
        
        self.obs_encoder_momentum = ObservationEncoder(
                            obs_shape=(self.args.frame_stack*self.args.channels,self.args.image_size,self.args.image_size), # (12,84,84)
                            state_size=self.args.state_size # 128
                            )

        self.obs_decoder = ObservationDecoder(
                            state_size=self.args.state_size, # 128
                            output_shape=(self.args.frame_stack*self.args.channels,self.args.image_size,self.args.image_size) # (12,84,84)
                            )

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

        self.reward_model = RewardModel(
                            state_size=self.args.state_size, # 128
                            hidden_size=self.args.hidden_size, # 256
                            )
        
        # 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
                            )
        
        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
                            )
        
        self.contrastive_head = ContrastiveHead(
                            hidden_size=self.args.hidden_size, # 256
                            )


        # model parameters
        self.world_model_parameters = list(self.obs_encoder.parameters()) + list(self.obs_decoder.parameters()) + \
                                list(self.value_model.parameters())  + list(self.transition_model.parameters()) + \
                                list(self.prjoection_head.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.past_transition_opt = torch.optim.Adam(self.past_transition_parameters, self.args.past_transition_lr)

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

        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):
        obs = self.env.reset()
        self.ob_mean = np.mean(obs, 0).astype(np.float32)
        self.ob_std = np.std(obs, 0).mean().astype(np.float32)
        #obs_clean = self.env_clean.reset()
        done = False

        #video = VideoRecorder(self.video_dir if args.save_video else None, resource_files=args.resource_files)
        for episode_count in tqdm.tqdm(range(episodes), desc='Collecting episodes'):
            if args.save_video:
                self.env.video.init(enabled=True)
                #self.env_clean.video.init(enabled=True)

            for i in range(self.args.episode_length):
                
                action = self.env.action_space.sample()

                next_obs, rew, done, _ = self.env.step(action)
                #next_obs_clean, _, done, _ = self.env_clean.step(action)

                self.data_buffer.add(obs, action, next_obs, rew, episode_count+1, done)
                #self.data_buffer_clean.add(obs_clean, action, next_obs_clean, episode_count+1, done)
                
                if args.save_video:
                    self.env.video.record(self.env)
                    #self.env_clean.video.record(self.env_clean)
                
                if done or i == self.args.episode_length-1:
                    obs = self.env.reset()
                    #obs_clean = self.env_clean.reset()
                    done=False
                else:
                    obs = next_obs
                    #obs_clean = next_obs_clean
            if args.save_video:
                self.env.video.save('noisy/%d.mp4' % episode_count)
                #self.env_clean.video.save('clean/%d.mp4' % episode_count)
        print("Collected {} random episodes".format(episode_count+1))

    def train(self):
        # collect experience
        self.collect_sequences(self.args.batch_size)

        # Group observations and next_observations by steps from past to present
        last_observations = torch.tensor(self.data_buffer.group_steps(self.data_buffer,"observations")).float()[:self.args.episode_length-1]
        current_observations = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"next_observations")).float()[:self.args.episode_length-1]
        next_observations = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"next_observations")).float()[1:]
        actions = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"actions",obs=False)).float()[:self.args.episode_length-1]
        next_actions = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"actions",obs=False)).float()[1:]
        rewards = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"rewards",obs=False)).float()[1:]

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

        # Train encoder
        step = 0
        total_steps = 10000
        metrics = {}
        while step < total_steps:
            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
                    idx = torch.randperm(current_observations[i].shape[0]) # random permutation on batch
                    random_time_index = torch.randint(0, self.args.episode_length-2, (1,)).item() # random time index
                    negative_current_observations = current_observations[random_time_index][idx]
                    self.negative_current_states_dict = self.obs_encoder(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, 
                                                                          self.current_states_dict,
                                                                          self.negative_current_states_dict,
                                                                          predicted_current_state_dict
                                                                          )
                    #likeli_loss = torch.tensor(likeli_loss.numpy(),dtype=torch.float32, requires_grad=True)
                    #ikeli_loss = likeli_loss.mean()
                    
                    # Calculate encoder loss
                    encoder_loss = self._past_encoder_loss(self.current_states_dict,
                                                        predicted_current_state_dict)

                    #total_ub_loss += ub_loss
                    #total_encoder_loss += encoder_loss
                    
                    # contrastive projection
                    vec_anchor = predicted_current_state_dict["sample"]
                    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 = labels = torch.arange(logits.shape[0]).long()
                    lb_loss = F.cross_entropy(logits, labels)
                    

                    # update models
                    """
                    print(likeli_loss)
                    for i in range(self.args.num_likelihood_updates):
                        self.past_transition_opt.zero_grad()
                        print(likeli_loss)
                        likeli_loss.backward()
                        nn.utils.clip_grad_norm_(self.past_transition_parameters, self.args.grad_clip_norm)
                        self.past_transition_opt.step()
                    print(encoder_loss, ub_loss, lb_loss, step)
                    """

                    world_model_loss = encoder_loss + ub_loss + lb_loss
                    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()
                    
                    """
                    if step % self.args.logging_freq:
                        metrics['Upper Bound Loss'] = ub_loss.item()
                        metrics['Encoder Loss'] = encoder_loss.item()
                        metrics["Lower Bound Loss"] = lb_loss.item()
                        metrics["World Model Loss"] = world_model_loss.item()
                        wandb.log(metrics)
                    """

                    # behaviour learning
                    with FreezeParameters(self.world_model_modules):
                        imagine_horizon = self.args.imagine_horizon #np.minimum(self.args.imagine_horizon, self.args.episode_length-1-i)
                        imagined_rollout = self.transition_model.imagine_rollout(self.current_states_dict["sample"].detach(), 
                                                                                self.next_action, self.history.detach(), 
                                                                                imagine_horizon)
                        #print(imagined_rollout["sample"].shape, imagined_rollout["distribution"][0].sample().shape)
                    
                    # actor loss
                    with FreezeParameters(self.world_model_modules + self.value_modules):
                        imag_rew_dist = self.reward_model(imagined_rollout["sample"])
                        target_imag_val_dist = self.target_value_model(imagined_rollout["sample"])

                        imag_rews = imag_rew_dist.mean
                        target_imag_vals = target_imag_val_dist.mean

                        discounts =  self.args.discount * torch.ones_like(imag_rews).detach()
                    
                    self.target_returns = self._compute_lambda_return(imag_rews[:-1], 
                                                               target_imag_vals[:-1], 
                                                               discounts[:-1] ,
                                                               self.args.td_lambda, 
                                                               target_imag_vals[-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.target_returns)

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

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

                    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()

                    step += 1

                    if step>total_steps:
                        print("Training finished")
                        break
                    #print(total_ub_loss, total_encoder_loss)



    def _upper_bound_minimization(self, last_states, current_states, negative_current_states, predicted_current_states):
        club_sample = CLUBSample(last_states,
                                 current_states,
                                 negative_current_states,
                                 predicted_current_states)
        likelihood_loss = club_sample.learning_loss()
        club_loss = club_sample()
        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.distributions.kl.kl_divergence(curr_states_dist, predicted_curr_states_dist).mean()

        return loss
    
    def get_features(self, x, momentum=False):
        import torchvision.transforms.functional as fn
        x = x/255.0 - 0.5  # Preprocessing
        
        if self.args.aug:
            x = T.RandomCrop((80, 80))(x)  # (None,80,80,4)
            x = T.functional.pad(x, (4, 4, 4, 4), "symmetric")  # (None,88,88,4)
            x = T.RandomCrop((84, 84))(x)  # (None,84,84,4)

        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

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

    dpi = DPI(args)
    dpi.train()