eb0215cf76
Improves typing in examples and tests, towards mypy passing there. Introduces the SpaceInfo utility
159 lines
6.3 KiB
Python
159 lines
6.3 KiB
Python
import argparse
|
|
import os
|
|
import pprint
|
|
from typing import cast
|
|
|
|
import gymnasium as 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 DiscreteSACPolicy
|
|
from tianshou.policy.base import BasePolicy
|
|
from tianshou.policy.modelfree.discrete_sac import DiscreteSACTrainingStats
|
|
from tianshou.trainer import OffpolicyTrainer
|
|
from tianshou.utils import TensorboardLogger
|
|
from tianshou.utils.net.common import Net
|
|
from tianshou.utils.net.discrete import Actor, Critic
|
|
from tianshou.utils.space_info import SpaceInfo
|
|
|
|
|
|
def get_args() -> argparse.Namespace:
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--task", type=str, default="CartPole-v0")
|
|
parser.add_argument("--reward-threshold", type=float, default=None)
|
|
parser.add_argument("--seed", type=int, default=1)
|
|
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("--alpha-lr", type=float, default=3e-4)
|
|
parser.add_argument("--gamma", type=float, default=0.95)
|
|
parser.add_argument("--tau", type=float, default=0.005)
|
|
parser.add_argument("--alpha", type=float, default=0.05)
|
|
parser.add_argument("--auto-alpha", action="store_true", default=False)
|
|
parser.add_argument("--epoch", type=int, default=5)
|
|
parser.add_argument("--step-per-epoch", type=int, default=10000)
|
|
parser.add_argument("--step-per-collect", type=int, default=10)
|
|
parser.add_argument("--update-per-step", type=float, default=0.1)
|
|
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=10)
|
|
parser.add_argument("--test-num", type=int, default=100)
|
|
parser.add_argument("--logdir", type=str, default="log")
|
|
parser.add_argument("--render", type=float, default=0.0)
|
|
parser.add_argument("--n-step", type=int, default=3)
|
|
parser.add_argument(
|
|
"--device",
|
|
type=str,
|
|
default="cuda" if torch.cuda.is_available() else "cpu",
|
|
)
|
|
return parser.parse_known_args()[0]
|
|
|
|
|
|
def test_discrete_sac(args: argparse.Namespace = get_args()) -> None:
|
|
env = gym.make(args.task)
|
|
env.action_space = cast(gym.spaces.Discrete, env.action_space)
|
|
|
|
space_info = SpaceInfo.from_env(env)
|
|
args.state_shape = space_info.observation_info.obs_shape
|
|
args.action_shape = space_info.action_info.action_shape
|
|
|
|
if args.reward_threshold is None:
|
|
default_reward_threshold = {"CartPole-v0": 170} # lower the goal
|
|
args.reward_threshold = default_reward_threshold.get(
|
|
args.task,
|
|
env.spec.reward_threshold if env.spec else None,
|
|
)
|
|
|
|
train_envs = DummyVectorEnv([lambda: gym.make(args.task) for _ in range(args.training_num)])
|
|
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
|
|
obs_dim = space_info.observation_info.obs_dim
|
|
action_dim = space_info.action_info.action_dim
|
|
net = Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
|
|
actor = Actor(net, args.action_shape, softmax_output=False, device=args.device).to(args.device)
|
|
actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
|
|
net_c1 = Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
|
|
critic1 = Critic(net_c1, last_size=action_dim, device=args.device).to(args.device)
|
|
critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
|
|
net_c2 = Net(obs_dim, hidden_sizes=args.hidden_sizes, device=args.device)
|
|
critic2 = Critic(net_c2, last_size=action_dim, device=args.device).to(args.device)
|
|
critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
|
|
|
|
# better not to use auto alpha in CartPole
|
|
if args.auto_alpha:
|
|
target_entropy = 0.98 * np.log(action_dim)
|
|
log_alpha = torch.zeros(1, requires_grad=True, device=args.device)
|
|
alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr)
|
|
args.alpha = (target_entropy, log_alpha, alpha_optim)
|
|
|
|
policy: DiscreteSACPolicy[DiscreteSACTrainingStats] = DiscreteSACPolicy(
|
|
actor=actor,
|
|
actor_optim=actor_optim,
|
|
critic=critic1,
|
|
action_space=env.action_space,
|
|
critic_optim=critic1_optim,
|
|
critic2=critic2,
|
|
critic2_optim=critic2_optim,
|
|
tau=args.tau,
|
|
gamma=args.gamma,
|
|
alpha=args.alpha,
|
|
estimation_step=args.n_step,
|
|
)
|
|
# collector
|
|
train_collector = Collector(
|
|
policy,
|
|
train_envs,
|
|
VectorReplayBuffer(args.buffer_size, len(train_envs)),
|
|
)
|
|
test_collector = Collector(policy, test_envs)
|
|
# train_collector.collect(n_step=args.buffer_size)
|
|
# log
|
|
log_path = os.path.join(args.logdir, args.task, "discrete_sac")
|
|
writer = SummaryWriter(log_path)
|
|
logger = TensorboardLogger(writer)
|
|
|
|
def save_best_fn(policy: BasePolicy) -> None:
|
|
torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))
|
|
|
|
def stop_fn(mean_rewards: float) -> bool:
|
|
return mean_rewards >= args.reward_threshold
|
|
|
|
# trainer
|
|
result = OffpolicyTrainer(
|
|
policy=policy,
|
|
train_collector=train_collector,
|
|
test_collector=test_collector,
|
|
max_epoch=args.epoch,
|
|
step_per_epoch=args.step_per_epoch,
|
|
step_per_collect=args.step_per_collect,
|
|
episode_per_test=args.test_num,
|
|
batch_size=args.batch_size,
|
|
stop_fn=stop_fn,
|
|
save_best_fn=save_best_fn,
|
|
logger=logger,
|
|
update_per_step=args.update_per_step,
|
|
test_in_train=False,
|
|
).run()
|
|
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)
|
|
collector_stats = collector.collect(n_episode=1, render=args.render)
|
|
print(collector_stats)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_discrete_sac()
|