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, Actor, ValueModel, RewardModel 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 ) self.action_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 ) 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 ) # 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) #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()