mirror of
https://github.com/wassname/curl.git
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291 lines
11 KiB
Python
291 lines
11 KiB
Python
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 curl_sac import CurlSacAgent
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from torchvision import transforms
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import apple_gym.env
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from diy_gym.utils import flatten, unflatten
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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='ApplePick-v0')
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parser.add_argument('--pre_transform_image_size', default=124, type=int)
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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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parser.add_argument('--render', action='store_true')
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# replay buffer
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parser.add_argument('--replay_buffer_capacity', default=10000, type=int)
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# train
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parser.add_argument('--agent', default='curl_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=32, type=int)
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parser.add_argument('--hidden_dim', default=1024, type=int)
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# eval
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parser.add_argument('--eval_freq', default=1000, 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=0.01, type=float) # try 0.05 or 0.1
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parser.add_argument('--critic_target_update_freq', default=2, type=int) # try to change it to 1 and retain 0.01 above
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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
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parser.add_argument('--encoder_type', default='mixed', 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=0.05, 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('--curl_latent_dim', default=128, type=int)
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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=0.1, type=float)
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parser.add_argument('--alpha_lr', default=1e-4, type=float)
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parser.add_argument('--alpha_beta', default=0.5, 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='./runs', type=str)
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parser.add_argument('--save_tb', 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('--save_model', default=False, action='store_true')
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parser.add_argument('--detach_encoder', default=False, action='store_true')
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parser.add_argument('--log_interval', default=100, type=int)
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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, args):
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all_ep_rewards = []
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def run_eval_loop(sample_stochastically=True):
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start_time = time.time()
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prefix = 'stochastic_' if sample_stochastically else ''
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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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# center crop image
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if args.encoder_type == 'mixed':
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state, img = utils.split_obs(obs)
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img = utils.center_crop_image(img, args.image_size)
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obs = utils.combine_obs(state, img)
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with utils.eval_mode(agent):
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if sample_stochastically:
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action = agent.sample_action(obs)
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else:
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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/' + prefix + 'episode_reward', episode_reward, step)
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all_ep_rewards.append(episode_reward)
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L.log('eval/' + prefix + 'eval_time', time.time() - start_time , step)
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mean_ep_reward = np.mean(all_ep_rewards)
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best_ep_reward = np.max(all_ep_rewards)
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L.log('eval/' + prefix + 'mean_episode_reward', mean_ep_reward, step)
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L.log('eval/' + prefix + 'best_episode_reward', best_ep_reward, step)
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run_eval_loop(sample_stochastically=False)
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L.dump(step)
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def make_agent(obs_shape, action_shape, args, device):
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if args.agent == 'curl_sac':
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return CurlSacAgent(
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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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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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num_layers=args.num_layers,
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num_filters=args.num_filters,
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log_interval=args.log_interval,
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detach_encoder=args.detach_encoder,
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curl_latent_dim=args.curl_latent_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 main():
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args = parse_args()
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if args.seed == -1:
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args.__dict__["seed"] = np.random.randint(1,1000000)
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utils.set_seed_everywhere(args.seed)
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env = gym.make(args.domain_name, render=args.render)
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# TODO action repeat wrapper?
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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_mixeds=(args.encoder_type == 'mixed'),
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# height=args.pre_transform_image_size,
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# width=args.pre_transform_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 == 'mixed':
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from apple_gym.env.wrappers import FrameStack, ImageState, PermuteImages
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env = FrameStack(PermuteImages(ImageState(env), keys=['img']), n=args.frame_stack, keys=['img'])
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# make directory
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ts = time.gmtime()
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ts = time.strftime("%m-%d", ts)
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env_name = args.domain_name
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exp_name = env_name + '-' + ts + '-im' + str(args.image_size) +'-b' \
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+ str(args.batch_size) + '-s' + str(args.seed) + '-' + args.encoder_type
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args.work_dir = args.work_dir + '/' + exp_name
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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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action_shape = env.action_space.shape
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# TODO, I need cropped aug shape now...
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# TODO I need to make split obs and combine obs?
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img = env.observation_space.sample()['img']
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img_aug = utils.center_crop_image(img, args.image_size)
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obs_shape = {'img': img_aug.shape, 'state': env.observation_space['state'].shape}
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replay_buffer = utils.ReplayBuffer(
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obs_space=env.observation_space,
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action_space=env.action_space,
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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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image_size=args.image_size,
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)
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agent = make_agent(
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obs_shape=obs_shape,
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action_shape=action_shape,
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args=args,
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device=device
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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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# 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,args)
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if args.save_model:
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agent.save_curl(model_dir, step)
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if args.save_buffer:
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replay_buffer.save(buffer_dir)
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if done:
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if step > 0:
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if step % args.log_interval == 0:
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L.log('train/duration', time.time() - start_time, step)
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L.dump(step)
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start_time = time.time()
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if step % args.log_interval == 0:
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L.log('train/episode_reward', episode_reward, step)
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obs = env.reset()
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assert env.observation_space.contains(obs), f'obs should be in space. ob={obs} space={env.observation_space}'
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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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if step % args.log_interval == 0:
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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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assert env.action_space.contains(action), f'obs should be in space. ob={action} space={env.action_space}'
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# run training update
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if step >= args.init_steps:
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num_updates = 1
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for _ in range(num_updates):
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agent.update(replay_buffer, L, step)
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next_obs, reward, done, _ = env.step(action)
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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)
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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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torch.multiprocessing.set_start_method('spawn')
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main()
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