import numpy as np import torch import argparse import os import gym import time import json import dmc2gym 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 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('--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=4, 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) # replay buffer parser.add_argument('--replay_buffer_capacity', default=50000, type=int) #100000 parser.add_argument('--episode_length', default=50, type=int) # train parser.add_argument('--agent', default='dpi', type=str, choices=['baseline', 'bisim', 'deepmdp', 'db', 'dpi', 'rpc']) parser.add_argument('--init_steps', default=1000, type=int) parser.add_argument('--num_train_steps', default=1000, type=int) parser.add_argument('--batch_size', default=200, 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('--load_encoder', default=None, type=str) parser.add_argument('--imagination_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 # environment setup self.env = make_env(self.args) self.env.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) # 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 video recorder #video = VideoRecorder(video_dir if args.save_video else None, resource_files=args.resource_files) #video.init(enabled=True) # 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_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_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_random_episodes(self, episodes): obs = self.env.reset() done = False for episode_count in range(episodes): video = VideoRecorder(self.video_dir if args.save_video else None, resource_files=args.resource_files) video.init(enabled=True) for i in range(self.args.episode_length): action = self.env.action_space.sample() next_obs, _, done, _ = self.env.step(action) self.data_buffer.add(obs, action, next_obs, episode_count+1, done) if args.save_video: video.record(self.env) if done: obs = self.env.reset() done=False else: obs = next_obs video.save('%d.mp4' % episode_count) print("Collected {} random episodes".format(episode_count+1)) #if args.save_video: # video.record(self.env) #video.save('%d.mp4' % step) #video.close() def train(self): # collect experience self.collect_random_episodes(self.args.batch_size) # Group observations and next_observations by steps observations = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"observations")).float() next_observations = torch.Tensor(self.data_buffer.group_steps(self.data_buffer,"next_observations")).float() # Train encoder previous_information_loss = 0 for i in range(self.args.episode_length): # Encode observations and next_observations self.features = self.obs_encoder(observations[i]) # (N,128) self.next_features = self.obs_encoder(next_observations[i]) # (N,128) # Calculate upper bound loss past_loss = previous_information_loss + self.upper_bound_minimization(self.features, self.next_features) previous_information_loss = past_loss print("past_loss: ", past_loss) def upper_bound_minimization(self, features, next_features): club_sample = CLUBSample(self.args.state_size, self.args.state_size, self.args.hidden_size) club_loss = club_sample(features, next_features) return club_loss if __name__ == '__main__': args = parse_args() dpi = DPI(args) dpi.train()