316 lines
11 KiB
Python
316 lines
11 KiB
Python
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import numpy as np
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import torch
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import argparse
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import os
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import math
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import gym
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import sys
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import random
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import time
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import json
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import dmc2gym
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import copy
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import utils
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from logger import Logger
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from video import VideoRecorder
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from sac import SACAgent
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from td3 import TD3Agent
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from ddpg import DDPGAgent
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def parse_args():
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parser = argparse.ArgumentParser()
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# environment
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parser.add_argument('--domain_name', default='cheetah')
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parser.add_argument('--task_name', default='run')
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parser.add_argument('--image_size', default=84, type=int)
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parser.add_argument('--action_repeat', default=1, type=int)
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parser.add_argument('--frame_stack', default=3, type=int)
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# replay buffer
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parser.add_argument('--replay_buffer_capacity', default=1000000, type=int)
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# train
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parser.add_argument('--agent', default='sac', type=str)
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parser.add_argument('--init_steps', default=1000, type=int)
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parser.add_argument('--num_train_steps', default=1000000, type=int)
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parser.add_argument('--batch_size', default=512, type=int)
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parser.add_argument('--hidden_dim', default=256, type=int)
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# eval
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parser.add_argument('--eval_freq', default=10000, type=int)
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parser.add_argument('--num_eval_episodes', default=10, type=int)
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# critic
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parser.add_argument('--critic_lr', default=1e-3, type=float)
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parser.add_argument('--critic_beta', default=0.9, type=float)
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parser.add_argument('--critic_tau', default=0.005, type=float)
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parser.add_argument('--critic_target_update_freq', default=2, type=int)
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# actor
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parser.add_argument('--actor_lr', default=1e-3, type=float)
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parser.add_argument('--actor_beta', default=0.9, type=float)
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parser.add_argument('--actor_log_std_min', default=-10, type=float)
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parser.add_argument('--actor_log_std_max', default=2, type=float)
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parser.add_argument('--actor_update_freq', default=2, type=int)
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# encoder/decoder
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parser.add_argument('--encoder_type', default='identity', type=str)
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parser.add_argument('--encoder_feature_dim', default=50, type=int)
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parser.add_argument('--encoder_lr', default=1e-3, type=float)
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parser.add_argument('--encoder_tau', default=0.005, type=float)
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parser.add_argument('--decoder_type', default='identity', type=str)
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parser.add_argument('--decoder_lr', default=1e-3, type=float)
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parser.add_argument('--decoder_update_freq', default=1, type=int)
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parser.add_argument('--decoder_latent_lambda', default=0.0, type=float)
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parser.add_argument('--decoder_weight_lambda', default=0.0, type=float)
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parser.add_argument('--decoder_kl_lambda', default=0.0, type=float)
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parser.add_argument('--num_layers', default=4, type=int)
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parser.add_argument('--num_filters', default=32, type=int)
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parser.add_argument('--freeze_encoder', default=False, action='store_true')
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parser.add_argument('--use_dynamics', default=False, action='store_true')
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# sac
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parser.add_argument('--discount', default=0.99, type=float)
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parser.add_argument('--init_temperature', default=0.01, type=float)
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parser.add_argument('--alpha_lr', default=1e-3, type=float)
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parser.add_argument('--alpha_beta', default=0.9, type=float)
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# td3
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parser.add_argument('--policy_noise', default=0.2, type=float)
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parser.add_argument('--expl_noise', default=0.1, type=float)
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parser.add_argument('--noise_clip', default=0.5, type=float)
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parser.add_argument('--tau', default=0.005, type=float)
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# misc
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parser.add_argument('--seed', default=1, type=int)
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parser.add_argument('--work_dir', default='.', type=str)
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parser.add_argument('--save_tb', default=False, action='store_true')
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parser.add_argument('--save_model', default=False, action='store_true')
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parser.add_argument('--save_buffer', default=False, action='store_true')
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parser.add_argument('--save_video', default=False, action='store_true')
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parser.add_argument('--pretrained_info', default=None, type=str)
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parser.add_argument('--pretrained_decoder', default=False, action='store_true')
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args = parser.parse_args()
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return args
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def evaluate(env, agent, video, num_episodes, L, step):
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for i in range(num_episodes):
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obs = env.reset()
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video.init(enabled=(i == 0))
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done = False
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episode_reward = 0
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while not done:
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with utils.eval_mode(agent):
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action = agent.select_action(obs)
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obs, reward, done, _ = env.step(action)
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video.record(env)
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episode_reward += reward
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video.save('%d.mp4' % step)
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L.log('eval/episode_reward', episode_reward, step)
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L.dump(step)
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def make_agent(obs_shape, state_shape, action_shape, args, device):
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if args.agent == 'sac':
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return SACAgent(
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obs_shape=obs_shape,
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state_shape=state_shape,
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action_shape=action_shape,
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device=device,
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hidden_dim=args.hidden_dim,
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discount=args.discount,
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init_temperature=args.init_temperature,
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alpha_lr=args.alpha_lr,
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alpha_beta=args.alpha_beta,
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actor_lr=args.actor_lr,
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actor_beta=args.actor_beta,
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actor_log_std_min=args.actor_log_std_min,
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actor_log_std_max=args.actor_log_std_max,
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actor_update_freq=args.actor_update_freq,
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critic_lr=args.critic_lr,
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critic_beta=args.critic_beta,
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critic_tau=args.critic_tau,
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critic_target_update_freq=args.critic_target_update_freq,
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encoder_type=args.encoder_type,
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encoder_feature_dim=args.encoder_feature_dim,
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encoder_lr=args.encoder_lr,
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encoder_tau=args.encoder_tau,
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decoder_type=args.decoder_type,
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decoder_lr=args.decoder_lr,
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decoder_update_freq=args.decoder_update_freq,
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decoder_latent_lambda=args.decoder_latent_lambda,
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decoder_weight_lambda=args.decoder_weight_lambda,
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decoder_kl_lambda=args.decoder_kl_lambda,
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num_layers=args.num_layers,
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num_filters=args.num_filters,
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freeze_encoder=args.freeze_encoder,
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use_dynamics=args.use_dynamics
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)
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elif args.agent == 'td3':
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return TD3Agent(
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obs_shape=obs_shape,
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action_shape=action_shape,
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device=device,
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discount=args.discount,
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tau=args.tau,
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policy_noise=args.policy_noise,
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noise_clip=args.noise_clip,
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expl_noise=args.expl_noise,
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actor_lr=args.actor_lr,
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critic_lr=args.critic_lr,
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encoder_type=args.encoder_type,
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encoder_feature_dim=args.encoder_feature_dim,
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actor_update_freq=args.actor_update_freq,
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target_update_freq=args.critic_target_update_freq
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)
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elif args.agent == 'ddpg':
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return DDPGAgent(
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obs_shape=obs_shape,
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action_shape=action_shape,
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device=device,
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discount=args.discount,
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tau=args.tau,
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actor_lr=args.actor_lr,
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critic_lr=args.critic_lr,
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encoder_type=args.encoder_type,
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encoder_feature_dim=args.encoder_feature_dim
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)
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else:
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assert 'agent is not supported: %s' % args.agent
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def load_pretrained_encoder(agent, pretrained_info, pretrained_decoder):
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path, version = pretrained_info.split(':')
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pretrained_agent = copy.deepcopy(agent)
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pretrained_agent.load(path, int(version))
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agent.critic.encoder.load_state_dict(
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pretrained_agent.critic.encoder.state_dict()
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)
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agent.actor.encoder.load_state_dict(
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pretrained_agent.actor.encoder.state_dict()
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)
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if pretrained_decoder:
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agent.decoder.load_state_dict(pretrained_agent.decoder.state_dict())
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return agent
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def main():
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args = parse_args()
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utils.set_seed_everywhere(args.seed)
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env = dmc2gym.make(
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domain_name=args.domain_name,
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task_name=args.task_name,
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seed=args.seed,
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visualize_reward=False,
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from_pixels=(args.encoder_type == 'pixel'),
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height=args.image_size,
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width=args.image_size,
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frame_skip=args.action_repeat
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)
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env.seed(args.seed)
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# stack several consecutive frames together
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if args.encoder_type == 'pixel':
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env = utils.FrameStack(env, k=args.frame_stack)
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utils.make_dir(args.work_dir)
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video_dir = utils.make_dir(os.path.join(args.work_dir, 'video'))
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model_dir = utils.make_dir(os.path.join(args.work_dir, 'model'))
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buffer_dir = utils.make_dir(os.path.join(args.work_dir, 'buffer'))
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video = VideoRecorder(video_dir if args.save_video else None)
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with open(os.path.join(args.work_dir, 'args.json'), 'w') as f:
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json.dump(vars(args), f, sort_keys=True, indent=4)
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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# the dmc2gym wrapper standardizes actions
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assert env.action_space.low.min() >= -1
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assert env.action_space.high.max() <= 1
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replay_buffer = utils.ReplayBuffer(
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obs_shape=env.observation_space.shape,
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state_shape=env.state_space.shape,
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action_shape=env.action_space.shape,
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capacity=args.replay_buffer_capacity,
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batch_size=args.batch_size,
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device=device
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)
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agent = make_agent(
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obs_shape=env.observation_space.shape,
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state_shape=env.state_space.shape,
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action_shape=env.action_space.shape,
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args=args,
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device=device
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)
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if args.pretrained_info is not None:
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agent = load_pretrained_encoder(
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agent, args.pretrained_info, args.pretrained_decoder
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)
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L = Logger(args.work_dir, use_tb=args.save_tb)
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episode, episode_reward, done = 0, 0, True
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start_time = time.time()
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for step in range(args.num_train_steps):
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if done:
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if step > 0:
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L.log('train/duration', time.time() - start_time, step)
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start_time = time.time()
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L.dump(step)
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# evaluate agent periodically
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if step % args.eval_freq == 0:
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L.log('eval/episode', episode, step)
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evaluate(env, agent, video, args.num_eval_episodes, L, step)
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if args.save_model:
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agent.save(model_dir, step)
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if args.save_buffer:
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replay_buffer.save(buffer_dir)
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L.log('train/episode_reward', episode_reward, step)
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obs = env.reset()
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done = False
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episode_reward = 0
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episode_step = 0
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episode += 1
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L.log('train/episode', episode, step)
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# sample action for data collection
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if step < args.init_steps:
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action = env.action_space.sample()
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else:
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with utils.eval_mode(agent):
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action = agent.sample_action(obs)
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# run training update
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if step >= args.init_steps:
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num_updates = args.init_steps if step == args.init_steps else 1
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for _ in range(num_updates):
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agent.update(replay_buffer, L, step)
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state = env.env.env._current_state
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next_obs, reward, done, _ = env.step(action)
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next_state = env.env.env._current_state.shape
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# allow infinit bootstrap
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done_bool = 0 if episode_step + 1 == env._max_episode_steps else float(
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done
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)
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episode_reward += reward
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replay_buffer.add(obs, action, reward, next_obs, done_bool, state, next_state)
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obs = next_obs
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episode_step += 1
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if __name__ == '__main__':
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main()
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