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 running
start_time = time.time()
for i in range(args.n_epochs):
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('TestEpRet', average_only=True)
logger.log_tabular('TestSuccess', average_only=True)
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"])
net.save_norm(logger_kwargs["output_dir"])
net.save_replay_buffer(logger_kwargs["output_dir"])
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 running
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)
# ensure the gripper close!
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()
# 防止gym中的最大step会返回done=True
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"])
