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, make_env, save_image
from models import ObservationEncoder, ObservationDecoder, TransitionModel, CLUBSample
from logger import Logger
from video import VideoRecorder
from dmc2gym.wrappers import set_global_var

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)
    # eval
    parser.add_argument('--eval_freq', default=10, type=int)  # TODO: master had 10000
    parser.add_argument('--num_eval_episodes', default=20, type=int)
    # critic
    parser.add_argument('--critic_lr', default=1e-3, type=float)
    parser.add_argument('--critic_beta', default=0.9, type=float)
    parser.add_argument('--critic_tau', default=0.005, type=float)
    parser.add_argument('--critic_target_update_freq', default=2, type=int)
    # actor
    parser.add_argument('--actor_lr', default=1e-3, 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)
    # 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('--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)
    # 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('--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)
    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)
        self.data_buffer_clean = 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):
        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
                            )

        # model parameters
        self.model_parameters = list(self.obs_encoder.parameters()) + list(self.obs_encoder_momentum.parameters()) + \
                                list(self.obs_decoder.parameters()) + list(self.transition_model.parameters())

        # optimizer
        self.optimizer = torch.optim.Adam(self.model_parameters, lr=self.args.encoder_lr)

        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()
        #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, 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:]

        # 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
        total_ub_loss = 0
        total_encoder_loss = 0
        for i in range(self.args.episode_length-1):
            if i > 0:
                # Encode observations and next_observations
                self.last_states_dict = self.obs_encoder(last_observations[i])
                self.current_states_dict = self.obs_encoder(current_observations[i])
                self.next_states_dict = self.obs_encoder_momentum(next_observations[i])
                self.action = 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
                ub_loss = self._upper_bound_minimization(self.last_states_dict, 
                                                         self.current_states_dict,
                                                         self.negative_current_states_dict,
                                                         predicted_current_state_dict
                                                         )
                
                # 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

                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"], self.action, self.history, imagine_horizon)
                print(imagine_horizon)
                #exit()

                #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)
        club_loss = club_sample()
        return 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 _past_encoder_loss(self, states, next_states, states_dist, next_states_dist, actions, history, step):
        # Imagine next state
        if step == 0:
            actions = torch.zeros(self.args.batch_size, self.env.action_space.shape[0]).float() # Zero action for first step
            imagined_next_states = self.transition_model.imagine_step(states, actions, history)
            self.history = imagined_next_states["history"]
        else:
            imagined_next_states = self.transition_model.imagine_step(states, actions, self.history) # (N,128)

        # State Distribution
        imagined_next_states_dist = imagined_next_states["distribution"]

        # KL divergence loss
        loss = torch.distributions.kl.kl_divergence(imagined_next_states_dist, next_states_dist["distribution"]).mean()

        return loss
    """
    
    def get_features(self, x, momentum=False):
        if self.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():
            x = (x.float() - self.ob_mean) / self.ob_std
            if momentum:
                x = self.obs_encoder(x).detach()
            else:
                x = self.obs_encoder_momentum(x)
        return x

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

    dpi = DPI(args)
    dpi.train()