4d4d0daf9e
* improve performance set one thread for NN replace detach() op with torch.no_grad() * fix pep 8 errors
113 lines
4.3 KiB
Python
113 lines
4.3 KiB
Python
import os
|
|
import gym
|
|
import torch
|
|
import pprint
|
|
import argparse
|
|
import numpy as np
|
|
from torch.utils.tensorboard import SummaryWriter
|
|
|
|
from tianshou.env import VectorEnv
|
|
from tianshou.policy import DDPGPolicy
|
|
from tianshou.trainer import offpolicy_trainer
|
|
from tianshou.data import Collector, ReplayBuffer
|
|
|
|
if __name__ == '__main__':
|
|
from net import Actor, Critic
|
|
else: # pytest
|
|
from test.continuous.net import Actor, Critic
|
|
|
|
|
|
def get_args():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--task', type=str, default='Pendulum-v0')
|
|
parser.add_argument('--run-id', type=str, default='test')
|
|
parser.add_argument('--seed', type=int, default=0)
|
|
parser.add_argument('--buffer-size', type=int, default=20000)
|
|
parser.add_argument('--actor-lr', type=float, default=1e-4)
|
|
parser.add_argument('--critic-lr', type=float, default=1e-3)
|
|
parser.add_argument('--gamma', type=float, default=0.99)
|
|
parser.add_argument('--tau', type=float, default=0.005)
|
|
parser.add_argument('--exploration-noise', type=float, default=0.1)
|
|
parser.add_argument('--epoch', type=int, default=20)
|
|
parser.add_argument('--step-per-epoch', type=int, default=2400)
|
|
parser.add_argument('--collect-per-step', type=int, default=4)
|
|
parser.add_argument('--batch-size', type=int, default=128)
|
|
parser.add_argument('--layer-num', type=int, default=1)
|
|
parser.add_argument('--training-num', type=int, default=8)
|
|
parser.add_argument('--test-num', type=int, default=100)
|
|
parser.add_argument('--logdir', type=str, default='log')
|
|
parser.add_argument('--render', type=float, default=0.)
|
|
parser.add_argument(
|
|
'--device', type=str,
|
|
default='cuda' if torch.cuda.is_available() else 'cpu')
|
|
args = parser.parse_known_args()[0]
|
|
return args
|
|
|
|
|
|
def test_ddpg(args=get_args()):
|
|
torch.set_num_threads(1) # we just need only one thread for NN
|
|
env = gym.make(args.task)
|
|
if args.task == 'Pendulum-v0':
|
|
env.spec.reward_threshold = -250
|
|
args.state_shape = env.observation_space.shape or env.observation_space.n
|
|
args.action_shape = env.action_space.shape or env.action_space.n
|
|
args.max_action = env.action_space.high[0]
|
|
# you can also use tianshou.env.SubprocVectorEnv
|
|
# train_envs = gym.make(args.task)
|
|
train_envs = VectorEnv(
|
|
[lambda: gym.make(args.task) for _ in range(args.training_num)])
|
|
# test_envs = gym.make(args.task)
|
|
test_envs = VectorEnv(
|
|
[lambda: gym.make(args.task) for _ in range(args.test_num)])
|
|
# seed
|
|
np.random.seed(args.seed)
|
|
torch.manual_seed(args.seed)
|
|
train_envs.seed(args.seed)
|
|
test_envs.seed(args.seed)
|
|
# model
|
|
actor = Actor(
|
|
args.layer_num, args.state_shape, args.action_shape,
|
|
args.max_action, args.device
|
|
).to(args.device)
|
|
actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
|
|
critic = Critic(
|
|
args.layer_num, args.state_shape, args.action_shape, args.device
|
|
).to(args.device)
|
|
critic_optim = torch.optim.Adam(critic.parameters(), lr=args.critic_lr)
|
|
policy = DDPGPolicy(
|
|
actor, actor_optim, critic, critic_optim,
|
|
args.tau, args.gamma, args.exploration_noise,
|
|
[env.action_space.low[0], env.action_space.high[0]],
|
|
reward_normalization=True, ignore_done=True)
|
|
# collector
|
|
train_collector = Collector(
|
|
policy, train_envs, ReplayBuffer(args.buffer_size))
|
|
test_collector = Collector(policy, test_envs)
|
|
# log
|
|
log_path = os.path.join(args.logdir, args.task, 'ddpg', args.run_id)
|
|
writer = SummaryWriter(log_path)
|
|
|
|
def stop_fn(x):
|
|
return x >= env.spec.reward_threshold
|
|
|
|
# trainer
|
|
result = offpolicy_trainer(
|
|
policy, train_collector, test_collector, args.epoch,
|
|
args.step_per_epoch, args.collect_per_step, args.test_num,
|
|
args.batch_size, stop_fn=stop_fn, writer=writer)
|
|
assert stop_fn(result['best_reward'])
|
|
train_collector.close()
|
|
test_collector.close()
|
|
if __name__ == '__main__':
|
|
pprint.pprint(result)
|
|
# Let's watch its performance!
|
|
env = gym.make(args.task)
|
|
collector = Collector(policy, env)
|
|
result = collector.collect(n_episode=1, render=args.render)
|
|
print(f'Final reward: {result["rew"]}, length: {result["len"]}')
|
|
collector.close()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_ddpg()
|