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train_SAC.py
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train_SAC.py
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#!/usr/bin/env python3
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import copy
import math
import os
import sys
import time
import pickle as pkl
import utils
import hydra
from logger import Logger
from replay_buffer import ReplayBuffer
class Workspace(object):
def __init__(self, cfg):
self.work_dir = os.getcwd()
print(f'workspace: {self.work_dir}')
self.cfg = cfg
self.logger = Logger(self.work_dir,
save_tb=cfg.log_save_tb,
log_frequency=cfg.log_frequency,
agent=cfg.agent.name)
utils.set_seed_everywhere(cfg.seed)
self.device = torch.device(cfg.device)
self.log_success = False
self.step = 0
# make env
if 'metaworld' in cfg.env:
self.env = utils.make_metaworld_env(cfg)
self.log_success = True
else:
self.env = utils.make_env(cfg)
cfg.agent.params.obs_dim = self.env.observation_space.shape[0]
cfg.agent.params.action_dim = self.env.action_space.shape[0]
cfg.agent.params.action_range = [
float(self.env.action_space.low.min()),
float(self.env.action_space.high.max())
]
self.agent = hydra.utils.instantiate(cfg.agent)
# no relabel
self.replay_buffer = ReplayBuffer(
self.env.observation_space.shape,
self.env.action_space.shape,
int(cfg.replay_buffer_capacity),
self.device)
meta_file = os.path.join(self.work_dir, 'metadata.pkl')
pkl.dump({'cfg': self.cfg}, open(meta_file, "wb"))
def evaluate(self):
average_episode_reward = 0
if self.log_success:
success_rate = 0
for episode in range(self.cfg.num_eval_episodes):
obs = self.env.reset()
self.agent.reset()
done = False
episode_reward = 0
if self.log_success:
episode_success = 0
while not done:
with utils.eval_mode(self.agent):
action = self.agent.act(obs, sample=False)
obs, reward, done, extra = self.env.step(action)
episode_reward += reward
if self.log_success:
episode_success = max(episode_success, extra['success'])
average_episode_reward += episode_reward
if self.log_success:
success_rate += episode_success
average_episode_reward /= self.cfg.num_eval_episodes
if self.log_success:
success_rate /= self.cfg.num_eval_episodes
success_rate *= 100.0
self.logger.log('eval/episode_reward', average_episode_reward,
self.step)
if self.log_success:
self.logger.log('eval/success_rate', success_rate,
self.step)
self.logger.dump(self.step)
def run(self):
episode, episode_reward, done = 0, 0, True
if self.log_success:
episode_success = 0
start_time = time.time()
fixed_start_time = time.time()
while self.step < self.cfg.num_train_steps:
if done:
if self.step > 0:
self.logger.log('train/duration',
time.time() - start_time, self.step)
self.logger.log('train/total_duration',
time.time() - fixed_start_time, self.step)
start_time = time.time()
self.logger.dump(
self.step, save=(self.step > self.cfg.num_seed_steps))
# evaluate agent periodically
if self.step > 0 and self.step % self.cfg.eval_frequency == 0:
self.logger.log('eval/episode', episode, self.step)
self.evaluate()
self.logger.log('train/episode_reward', episode_reward,
self.step)
if self.log_success:
self.logger.log('train/episode_success', episode_success,
self.step)
obs = self.env.reset()
self.agent.reset()
done = False
episode_reward = 0
if self.log_success:
episode_success = 0
episode_step = 0
episode += 1
self.logger.log('train/episode', episode, self.step)
# sample action for data collection
if self.step < self.cfg.num_seed_steps:
action = self.env.action_space.sample()
else:
with utils.eval_mode(self.agent):
action = self.agent.act(obs, sample=True)
# run training update
if self.step == (self.cfg.num_seed_steps + self.cfg.num_unsup_steps) and self.cfg.num_unsup_steps > 0:
# reset Q due to unsuperivsed exploration
self.agent.reset_critic()
# update agent
self.agent.update_after_reset(
self.replay_buffer, self.logger, self.step,
gradient_update=self.cfg.reset_update,
policy_update=True)
elif self.step > self.cfg.num_seed_steps + self.cfg.num_unsup_steps:
self.agent.update(self.replay_buffer, self.logger, self.step)
# unsupervised exploration
elif self.step > self.cfg.num_seed_steps:
self.agent.update_state_ent(self.replay_buffer, self.logger, self.step,
gradient_update=1, K=self.cfg.topK)
next_obs, reward, done, extra = self.env.step(action)
# allow infinite bootstrap
done = float(done)
done_no_max = 0 if episode_step + 1 == self.env._max_episode_steps else done
episode_reward += reward
if self.log_success:
episode_success = max(episode_success, extra['success'])
self.replay_buffer.add(
obs, action,
reward, next_obs, done,
done_no_max)
obs = next_obs
episode_step += 1
self.step += 1
self.agent.save(self.work_dir, self.step)
@hydra.main(config_path='config/train.yaml', strict=True)
def main(cfg):
workspace = Workspace(cfg)
workspace.run()
if __name__ == '__main__':
main()