fc251ab0b8
* add makefile * bump version * add isort and yapf * update contributing.md * update PR template * spelling check
130 lines
4.3 KiB
Python
130 lines
4.3 KiB
Python
import argparse
|
|
import os
|
|
import pprint
|
|
|
|
import gym
|
|
import numpy as np
|
|
import torch
|
|
from torch.utils.tensorboard import SummaryWriter
|
|
|
|
from tianshou.data import Collector, VectorReplayBuffer
|
|
from tianshou.env import DummyVectorEnv
|
|
from tianshou.policy import PGPolicy
|
|
from tianshou.trainer import onpolicy_trainer
|
|
from tianshou.utils import TensorboardLogger
|
|
from tianshou.utils.net.common import Net
|
|
|
|
|
|
def get_args():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--task', type=str, default='CartPole-v0')
|
|
parser.add_argument('--seed', type=int, default=1)
|
|
parser.add_argument('--buffer-size', type=int, default=20000)
|
|
parser.add_argument('--lr', type=float, default=1e-3)
|
|
parser.add_argument('--gamma', type=float, default=0.95)
|
|
parser.add_argument('--epoch', type=int, default=10)
|
|
parser.add_argument('--step-per-epoch', type=int, default=40000)
|
|
parser.add_argument('--episode-per-collect', type=int, default=8)
|
|
parser.add_argument('--repeat-per-collect', type=int, default=2)
|
|
parser.add_argument('--batch-size', type=int, default=64)
|
|
parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[64, 64])
|
|
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('--rew-norm', type=int, default=1)
|
|
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_pg(args=get_args()):
|
|
env = gym.make(args.task)
|
|
args.state_shape = env.observation_space.shape or env.observation_space.n
|
|
args.action_shape = env.action_space.shape or env.action_space.n
|
|
# train_envs = gym.make(args.task)
|
|
# you can also use tianshou.env.SubprocVectorEnv
|
|
train_envs = DummyVectorEnv(
|
|
[lambda: gym.make(args.task) for _ in range(args.training_num)]
|
|
)
|
|
# test_envs = gym.make(args.task)
|
|
test_envs = DummyVectorEnv(
|
|
[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
|
|
net = Net(
|
|
args.state_shape,
|
|
args.action_shape,
|
|
hidden_sizes=args.hidden_sizes,
|
|
device=args.device,
|
|
softmax=True
|
|
).to(args.device)
|
|
optim = torch.optim.Adam(net.parameters(), lr=args.lr)
|
|
dist = torch.distributions.Categorical
|
|
policy = PGPolicy(
|
|
net,
|
|
optim,
|
|
dist,
|
|
args.gamma,
|
|
reward_normalization=args.rew_norm,
|
|
action_space=env.action_space
|
|
)
|
|
for m in net.modules():
|
|
if isinstance(m, torch.nn.Linear):
|
|
# orthogonal initialization
|
|
torch.nn.init.orthogonal_(m.weight, gain=np.sqrt(2))
|
|
torch.nn.init.zeros_(m.bias)
|
|
# collector
|
|
train_collector = Collector(
|
|
policy, train_envs, VectorReplayBuffer(args.buffer_size, len(train_envs))
|
|
)
|
|
test_collector = Collector(policy, test_envs)
|
|
# log
|
|
log_path = os.path.join(args.logdir, args.task, 'pg')
|
|
writer = SummaryWriter(log_path)
|
|
logger = TensorboardLogger(writer)
|
|
|
|
def save_fn(policy):
|
|
torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
|
|
|
|
def stop_fn(mean_rewards):
|
|
return mean_rewards >= env.spec.reward_threshold
|
|
|
|
# trainer
|
|
result = onpolicy_trainer(
|
|
policy,
|
|
train_collector,
|
|
test_collector,
|
|
args.epoch,
|
|
args.step_per_epoch,
|
|
args.repeat_per_collect,
|
|
args.test_num,
|
|
args.batch_size,
|
|
episode_per_collect=args.episode_per_collect,
|
|
stop_fn=stop_fn,
|
|
save_fn=save_fn,
|
|
logger=logger
|
|
)
|
|
assert stop_fn(result['best_reward'])
|
|
|
|
if __name__ == '__main__':
|
|
pprint.pprint(result)
|
|
# Let's watch its performance!
|
|
env = gym.make(args.task)
|
|
policy.eval()
|
|
collector = Collector(policy, env)
|
|
result = collector.collect(n_episode=1, render=args.render)
|
|
rews, lens = result["rews"], result["lens"]
|
|
print(f"Final reward: {rews.mean()}, length: {lens.mean()}")
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_pg()
|