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train.py
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train.py
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import numpy as np
import torch
import argparse
import os
import math
import gym
import sys
import random
import time
import json
import dmc2gym
import copy
import utils
from logger import Logger
from video import VideoRecorder
from curl_sac import RadSacAgent
from torchvision import transforms
import data_augs as rad
def parse_args():
parser = argparse.ArgumentParser()
# environment
parser.add_argument('--domain_name', default='cartpole')
parser.add_argument('--task_name', default='swingup')
parser.add_argument('--pre_transform_image_size', default=100, type=int)
parser.add_argument('--image_size', default=84, type=int)
parser.add_argument('--action_repeat', default=1, type=int)
parser.add_argument('--frame_stack', default=3, type=int)
# replay buffer
parser.add_argument('--replay_buffer_capacity', default=100000, type=int)
# train
parser.add_argument('--agent', default='rad_sac', type=str)
parser.add_argument('--init_steps', default=1000, type=int)
parser.add_argument('--num_train_steps', default=1000000, type=int)
parser.add_argument('--batch_size', default=32, type=int)
parser.add_argument('--hidden_dim', default=1024, type=int)
# eval
parser.add_argument('--eval_freq', default=1000, type=int)
parser.add_argument('--num_eval_episodes', default=10, 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=0.01, type=float) # try 0.05 or 0.1
parser.add_argument('--critic_target_update_freq', default=2, type=int) # try to change it to 1 and retain 0.01 above
# 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
parser.add_argument('--encoder_type', default='pixel', type=str)
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=0.05, type=float)
parser.add_argument('--num_layers', default=4, type=int)
parser.add_argument('--num_filters', default=32, type=int)
parser.add_argument('--latent_dim', default=128, type=int)
# sac
parser.add_argument('--discount', default=0.99, type=float)
parser.add_argument('--init_temperature', default=0.1, type=float)
parser.add_argument('--alpha_lr', default=1e-4, type=float)
parser.add_argument('--alpha_beta', default=0.5, 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_buffer', default=False, action='store_true')
parser.add_argument('--save_video', default=False, action='store_true')
parser.add_argument('--save_model', default=False, action='store_true')
parser.add_argument('--detach_encoder', default=False, action='store_true')
# data augs
parser.add_argument('--data_augs', default='crop', type=str)
parser.add_argument('--log_interval', default=100, type=int)
args = parser.parse_args()
return args
def evaluate(env, agent, video, num_episodes, L, step, args):
all_ep_rewards = []
def run_eval_loop(sample_stochastically=True):
start_time = time.time()
prefix = 'stochastic_' if sample_stochastically else ''
for i in range(num_episodes):
obs = env.reset()
video.init(enabled=(i == 0))
done = False
episode_reward = 0
while not done:
# center crop image
if args.encoder_type == 'pixel' and 'crop' in args.data_augs:
obs = utils.center_crop_image(obs,args.image_size)
if args.encoder_type == 'pixel' and 'translate' in args.data_augs:
# first crop the center with pre_image_size
obs = utils.center_crop_image(obs, args.pre_transform_image_size)
# then translate cropped to center
obs = utils.center_translate(obs, args.image_size)
with utils.eval_mode(agent):
if sample_stochastically:
action = agent.sample_action(obs / 255.)
else:
action = agent.select_action(obs / 255.)
obs, reward, done, _ = env.step(action)
video.record(env)
episode_reward += reward
video.save('%d.mp4' % step)
L.log('eval/' + prefix + 'episode_reward', episode_reward, step)
all_ep_rewards.append(episode_reward)
L.log('eval/' + prefix + 'eval_time', time.time()-start_time , step)
mean_ep_reward = np.mean(all_ep_rewards)
best_ep_reward = np.max(all_ep_rewards)
std_ep_reward = np.std(all_ep_rewards)
L.log('eval/' + prefix + 'mean_episode_reward', mean_ep_reward, step)
L.log('eval/' + prefix + 'best_episode_reward', best_ep_reward, step)
filename = args.work_dir + '/' + args.domain_name + '--'+args.task_name + '-' + args.data_augs + '--s' + str(args.seed) + '--eval_scores.npy'
key = args.domain_name + '-' + args.task_name + '-' + args.data_augs
try:
log_data = np.load(filename,allow_pickle=True)
log_data = log_data.item()
except:
log_data = {}
if key not in log_data:
log_data[key] = {}
log_data[key][step] = {}
log_data[key][step]['step'] = step
log_data[key][step]['mean_ep_reward'] = mean_ep_reward
log_data[key][step]['max_ep_reward'] = best_ep_reward
log_data[key][step]['std_ep_reward'] = std_ep_reward
log_data[key][step]['env_step'] = step * args.action_repeat
np.save(filename,log_data)
run_eval_loop(sample_stochastically=False)
L.dump(step)
def make_agent(obs_shape, action_shape, args, device):
if args.agent == 'rad_sac':
return RadSacAgent(
obs_shape=obs_shape,
action_shape=action_shape,
device=device,
hidden_dim=args.hidden_dim,
discount=args.discount,
init_temperature=args.init_temperature,
alpha_lr=args.alpha_lr,
alpha_beta=args.alpha_beta,
actor_lr=args.actor_lr,
actor_beta=args.actor_beta,
actor_log_std_min=args.actor_log_std_min,
actor_log_std_max=args.actor_log_std_max,
actor_update_freq=args.actor_update_freq,
critic_lr=args.critic_lr,
critic_beta=args.critic_beta,
critic_tau=args.critic_tau,
critic_target_update_freq=args.critic_target_update_freq,
encoder_type=args.encoder_type,
encoder_feature_dim=args.encoder_feature_dim,
encoder_lr=args.encoder_lr,
encoder_tau=args.encoder_tau,
num_layers=args.num_layers,
num_filters=args.num_filters,
log_interval=args.log_interval,
detach_encoder=args.detach_encoder,
latent_dim=args.latent_dim,
data_augs=args.data_augs
)
else:
assert 'agent is not supported: %s' % args.agent
def main():
args = parse_args()
if args.seed == -1:
args.__dict__["seed"] = np.random.randint(1,1000000)
utils.set_seed_everywhere(args.seed)
pre_transform_image_size = args.pre_transform_image_size if 'crop' in args.data_augs else args.image_size
pre_image_size = args.pre_transform_image_size # record the pre transform image size for translation
env = dmc2gym.make(
domain_name=args.domain_name,
task_name=args.task_name,
seed=args.seed,
visualize_reward=False,
from_pixels=(args.encoder_type == 'pixel'),
height=pre_transform_image_size,
width=pre_transform_image_size,
frame_skip=args.action_repeat
)
env.seed(args.seed)
# stack several consecutive frames together
if args.encoder_type == 'pixel':
env = utils.FrameStack(env, k=args.frame_stack)
# make directory
ts = time.gmtime()
ts = time.strftime("%m-%d", ts)
env_name = args.domain_name + '-' + args.task_name
exp_name = env_name + '-' + ts + '-im' + str(args.image_size) +'-b' \
+ str(args.batch_size) + '-s' + str(args.seed) + '-' + args.encoder_type
args.work_dir = args.work_dir + '/' + exp_name
utils.make_dir(args.work_dir)
video_dir = utils.make_dir(os.path.join(args.work_dir, 'video'))
model_dir = utils.make_dir(os.path.join(args.work_dir, 'model'))
buffer_dir = utils.make_dir(os.path.join(args.work_dir, 'buffer'))
video = VideoRecorder(video_dir if args.save_video else None)
with open(os.path.join(args.work_dir, 'args.json'), 'w') as f:
json.dump(vars(args), f, sort_keys=True, indent=4)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
action_shape = env.action_space.shape
if args.encoder_type == 'pixel':
obs_shape = (3*args.frame_stack, args.image_size, args.image_size)
pre_aug_obs_shape = (3*args.frame_stack,pre_transform_image_size,pre_transform_image_size)
else:
obs_shape = env.observation_space.shape
pre_aug_obs_shape = obs_shape
replay_buffer = utils.ReplayBuffer(
obs_shape=pre_aug_obs_shape,
action_shape=action_shape,
capacity=args.replay_buffer_capacity,
batch_size=args.batch_size,
device=device,
image_size=args.image_size,
pre_image_size=pre_image_size,
)
agent = make_agent(
obs_shape=obs_shape,
action_shape=action_shape,
args=args,
device=device
)
L = Logger(args.work_dir, use_tb=args.save_tb)
episode, episode_reward, done = 0, 0, True
start_time = time.time()
for step in range(args.num_train_steps):
# evaluate agent periodically
if step % args.eval_freq == 0:
L.log('eval/episode', episode, step)
evaluate(env, agent, video, args.num_eval_episodes, L, step,args)
if args.save_model:
agent.save_curl(model_dir, step)
if args.save_buffer:
replay_buffer.save(buffer_dir)
if done:
if step > 0:
if step % args.log_interval == 0:
L.log('train/duration', time.time() - start_time, step)
L.dump(step)
start_time = time.time()
if step % args.log_interval == 0:
L.log('train/episode_reward', episode_reward, step)
obs = env.reset()
done = False
episode_reward = 0
episode_step = 0
episode += 1
if step % args.log_interval == 0:
L.log('train/episode', episode, step)
# sample action for data collection
if step < args.init_steps:
action = env.action_space.sample()
else:
with utils.eval_mode(agent):
action = agent.sample_action(obs / 255.)
# run training update
if step >= args.init_steps:
num_updates = 1
for _ in range(num_updates):
agent.update(replay_buffer, L, step)
next_obs, reward, done, _ = env.step(action)
# allow infinit bootstrap
done_bool = 0 if episode_step + 1 == env._max_episode_steps else float(
done
)
episode_reward += reward
replay_buffer.add(obs, action, reward, next_obs, done_bool)
obs = next_obs
episode_step += 1
if __name__ == '__main__':
torch.multiprocessing.set_start_method('spawn')
main()