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train_tf1_mpi.py
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train_tf1_mpi.py
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import numpy as np
import gym
import os, sys
from tf1_arguments import get_args
from mpi4py import MPI
from subprocess import CalledProcessError
import time
from spinup_utils.logx import setup_logger_kwargs, colorize
from spinup_utils.logx import EpochLogger
from spinup_utils.print_logger import Logger
from spinup_utils.mpi_tools import mpi_fork, mpi_avg, proc_id, mpi_statistics_scalar, num_procs
from spinup_utils.mpi_tf import MpiAdamOptimizer, sync_all_params
import tensorflow as tf
import warnings
warnings.filterwarnings("ignore")
"""
train the agent, the MPI part code is copy from openai baselines(https://github.com/openai/baselines/blob/master/baselines/her)
but I ignore it~
"""
def obs2state(obs, key_list=['observation', 'desired_goal']):
if type(obs) == dict:
s = np.concatenate(([obs[key] for key in key_list]
))
elif type(obs) == np.ndarray:
s = obs[:]
else:
s = obs[:]
return s
def trainer(net, env, args):
# logger
exp_name = args.exp_name+'_'+args.RL_name+'_'+args.env_name
logger_kwargs = setup_logger_kwargs(exp_name=exp_name,
seed=args.seed,
output_dir=args.output_dir + "/")
logger = EpochLogger(**logger_kwargs)
if proc_id() == 0:
sys.stdout = Logger(logger_kwargs["output_dir"] + "/print.log",
sys.stdout)
logger.save_config(locals(), __file__)
# start trainning
start_time = time.time()
for i in range(args.n_epochs):
for c in range(args.n_cycles):
obs = env.reset()
episode_trans = []
s = obs2state(obs)
ep_reward = 0
real_ep_reward = 0
episode_time = time.time()
success = []
for j in range(args.n_steps):
a = net.get_action(s, noise_scale=args.noise_ps)
# a = net.get_action(s)
# a = noise.add_noise(a)
if np.random.random() < args.random_eps:
a = np.random.uniform(low=-net.a_bound,
high=net.a_bound,
size=net.act_dim)
a = np.clip(a, -net.a_bound, net.a_bound)
try:
obs_next, r, done, info = env.step(a)
success.append(info["is_success"])
except Exception as e:
success.append(int(done))
s_ = obs2state(obs_next)
# visualization
if args.render and i % 3 == 0 and c % 20 == 0:
env.render()
done = False if j == args.n_steps - 1 else done
if not args.her:
net.store_transition((s, a, r, s_, done))
episode_trans.append([obs, a, r, obs_next, done, info])
s = s_
obs = obs_next
ep_reward += r
real_ep_reward += r
if args.her:
net.save_episode(episode_trans=episode_trans,
reward_func=env.compute_reward,
obs2state=obs2state)
logger.store(EpRet=ep_reward)
logger.store(EpRealRet=real_ep_reward)
for _ in range(40):
outs = net.learn(args.batch_size,
args.base_lr,
args.base_lr * 2,
)
if outs[1] is not None:
logger.store(Q1=outs[1])
logger.store(Q2=outs[2])
if 0.0 < sum(success) < args.n_steps:
print("epoch:", i,
"\tep:", c,
"\tep_rew:", ep_reward,
"\ttime:", np.round(time.time()-episode_time, 3),
'\tdone:', sum(success))
test_ep_reward, logger = net.test_agent(args=args,
env=env,
n=10,
logger=logger,
obs2state=obs2state,
)
logger.store(TestEpRet=test_ep_reward)
logger.log_tabular('Epoch', i)
logger.log_tabular('EpRet', average_only=True)
logger.log_tabular('EpRealRet', average_only=True)
logger.log_tabular('TestEpRet', average_only=True)
logger.log_tabular('Q1', with_min_and_max=True)
logger.log_tabular('Q2', average_only=True)
logger.log_tabular('TestSuccess', average_only=True)
logger.log_tabular('TotalEnvInteracts', i * args.n_cycles * args.n_steps + c * args.n_steps + j + 1)
logger.log_tabular('TotalTime', time.time() - start_time)
logger.dump_tabular()
print(colorize("the experience %s is end" % logger.output_file.name,
'green', bold=True))
net.save_simple_network(logger_kwargs["output_dir"])
def launch(net, args):
env = gym.make(args.env_name)
# 确保不同进程的随机种子不同！
seed = args.seed
seed += 10000 * proc_id()
env.seed(seed)
np.random.seed(seed)
try:
s_dim = env.observation_space.shape[0]
except:
s_dim = env.observation_space.spaces['observation'].shape[0] + \
env.observation_space.spaces['desired_goal'].shape[0]
act_dim = env.action_space.shape[0]
a_bound = env.action_space.high[0]
net = net(act_dim=act_dim,
obs_dim=s_dim,
a_bound=a_bound,
per_flag=args.per,
her_flag=args.her,
action_l2=args.action_l2,
state_norm=args.state_norm,
gamma=args.gamma,
sess_opt=args.sess_opt,
seed=args.seed,
clip_return=args.clip_return,
)
# 同步强化网络的参数，如果网络不仅有强化的，可能会出问题；
sess = tf.Session()
sess.run(tf.global_variables_initializer())
# Sync params across processes
sess.run(sync_all_params())
trainer(net, env, args)
if __name__ == '__main__':
# os.environ['OMP_NUM_THREADS'] = '1'
# os.environ['MKL_NUM_THREADS'] = '1'
# 如果CPU比较多，GPU不够的话，建议只使用CPU~
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
# get the params
args = get_args()
# run parallel code with mpi
mpi_fork(args.cpu)
from algos.tf1.td3_sp.MPI_TD3_per_her import TD3
from algos.tf1.ddpg_sp.MPI_DDPG_per_her import DDPG
from algos.tf1.sac_sp.MPI_SAC_per_her import SAC
from algos.tf1.sac_auto.MPI_sac_auto_per_her import SAC_AUTO
RL_list = [TD3, DDPG, SAC, SAC_AUTO]
[launch(net=net, args=args) for net in RL_list if net.__name__ == args.RL_name]