8a0629ded6
Closes #952 - `SamplingConfig` supports `batch_size=None`. #1077 - tests and examples are covered by `mypy`. #1077 - `NetBase` is more used, stricter typing by making it generic. #1077 - `utils.net.common.Recurrent` now receives and returns a `RecurrentStateBatch` instead of a dict. #1077 --------- Co-authored-by: Michael Panchenko <m.panchenko@appliedai.de>
211 lines
8.1 KiB
Python
211 lines
8.1 KiB
Python
import argparse
|
|
import os
|
|
import pprint
|
|
|
|
import gymnasium as gym
|
|
import numpy as np
|
|
import torch
|
|
from torch.distributions import Distribution, Independent, Normal
|
|
from torch.utils.tensorboard import SummaryWriter
|
|
|
|
from tianshou.data import Collector, VectorReplayBuffer
|
|
from tianshou.env import DummyVectorEnv
|
|
from tianshou.policy import PPOPolicy
|
|
from tianshou.policy.base import BasePolicy
|
|
from tianshou.policy.modelfree.ppo import PPOTrainingStats
|
|
from tianshou.trainer import OnpolicyTrainer
|
|
from tianshou.utils import TensorboardLogger
|
|
from tianshou.utils.net.common import ActorCritic, Net
|
|
from tianshou.utils.net.continuous import ActorProb, Critic
|
|
from tianshou.utils.space_info import SpaceInfo
|
|
|
|
|
|
def get_args() -> argparse.Namespace:
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--task", type=str, default="Pendulum-v1")
|
|
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("--lr", type=float, default=1e-3)
|
|
parser.add_argument("--gamma", type=float, default=0.95)
|
|
parser.add_argument("--epoch", type=int, default=5)
|
|
parser.add_argument("--step-per-epoch", type=int, default=150000)
|
|
parser.add_argument("--episode-per-collect", type=int, default=16)
|
|
parser.add_argument("--repeat-per-collect", type=int, default=2)
|
|
parser.add_argument("--batch-size", type=int, default=128)
|
|
parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[64, 64])
|
|
parser.add_argument("--training-num", type=int, default=16)
|
|
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(
|
|
"--device",
|
|
type=str,
|
|
default="cuda" if torch.cuda.is_available() else "cpu",
|
|
)
|
|
# ppo special
|
|
parser.add_argument("--vf-coef", type=float, default=0.25)
|
|
parser.add_argument("--ent-coef", type=float, default=0.0)
|
|
parser.add_argument("--eps-clip", type=float, default=0.2)
|
|
parser.add_argument("--max-grad-norm", type=float, default=0.5)
|
|
parser.add_argument("--gae-lambda", type=float, default=0.95)
|
|
parser.add_argument("--rew-norm", type=int, default=1)
|
|
parser.add_argument("--dual-clip", type=float, default=None)
|
|
parser.add_argument("--value-clip", type=int, default=1)
|
|
parser.add_argument("--norm-adv", type=int, default=1)
|
|
parser.add_argument("--recompute-adv", type=int, default=0)
|
|
parser.add_argument("--resume", action="store_true")
|
|
parser.add_argument("--save-interval", type=int, default=4)
|
|
return parser.parse_known_args()[0]
|
|
|
|
|
|
def test_ppo(args: argparse.Namespace = get_args()) -> None:
|
|
env = gym.make(args.task)
|
|
|
|
space_info = SpaceInfo.from_env(env)
|
|
args.state_shape = space_info.observation_info.obs_shape
|
|
args.action_shape = space_info.action_info.action_shape
|
|
args.max_action = space_info.action_info.max_action
|
|
|
|
if args.reward_threshold is None:
|
|
default_reward_threshold = {"Pendulum-v0": -250, "Pendulum-v1": -250}
|
|
args.reward_threshold = default_reward_threshold.get(
|
|
args.task,
|
|
env.spec.reward_threshold if env.spec else None,
|
|
)
|
|
# you can also use tianshou.env.SubprocVectorEnv
|
|
# train_envs = gym.make(args.task)
|
|
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(state_shape=args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
|
|
actor = ActorProb(net, args.action_shape, unbounded=True, device=args.device).to(args.device)
|
|
critic = Critic(
|
|
Net(state_shape=args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device),
|
|
device=args.device,
|
|
).to(args.device)
|
|
actor_critic = ActorCritic(actor, critic)
|
|
# orthogonal initialization
|
|
for m in actor_critic.modules():
|
|
if isinstance(m, torch.nn.Linear):
|
|
torch.nn.init.orthogonal_(m.weight)
|
|
torch.nn.init.zeros_(m.bias)
|
|
optim = torch.optim.Adam(actor_critic.parameters(), lr=args.lr)
|
|
|
|
# replace DiagGuassian with Independent(Normal) which is equivalent
|
|
# pass *logits to be consistent with policy.forward
|
|
def dist(loc_scale: tuple[torch.Tensor, torch.Tensor]) -> Distribution:
|
|
loc, scale = loc_scale
|
|
return Independent(Normal(loc, scale), 1)
|
|
|
|
policy: PPOPolicy[PPOTrainingStats] = PPOPolicy(
|
|
actor=actor,
|
|
critic=critic,
|
|
optim=optim,
|
|
dist_fn=dist,
|
|
discount_factor=args.gamma,
|
|
max_grad_norm=args.max_grad_norm,
|
|
eps_clip=args.eps_clip,
|
|
vf_coef=args.vf_coef,
|
|
ent_coef=args.ent_coef,
|
|
reward_normalization=args.rew_norm,
|
|
advantage_normalization=args.norm_adv,
|
|
recompute_advantage=args.recompute_adv,
|
|
dual_clip=args.dual_clip,
|
|
value_clip=args.value_clip,
|
|
gae_lambda=args.gae_lambda,
|
|
action_space=env.action_space,
|
|
)
|
|
# 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, "ppo")
|
|
writer = SummaryWriter(log_path)
|
|
logger = TensorboardLogger(writer, save_interval=args.save_interval)
|
|
|
|
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
|
|
|
|
def save_checkpoint_fn(epoch: int, env_step: int, gradient_step: int) -> str:
|
|
# see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html
|
|
ckpt_path = os.path.join(log_path, "checkpoint.pth")
|
|
# Example: saving by epoch num
|
|
# ckpt_path = os.path.join(log_path, f"checkpoint_{epoch}.pth")
|
|
torch.save(
|
|
{
|
|
"model": policy.state_dict(),
|
|
"optim": optim.state_dict(),
|
|
},
|
|
ckpt_path,
|
|
)
|
|
return ckpt_path
|
|
|
|
if args.resume:
|
|
# load from existing checkpoint
|
|
print(f"Loading agent under {log_path}")
|
|
ckpt_path = os.path.join(log_path, "checkpoint.pth")
|
|
if os.path.exists(ckpt_path):
|
|
checkpoint = torch.load(ckpt_path, map_location=args.device)
|
|
policy.load_state_dict(checkpoint["model"])
|
|
optim.load_state_dict(checkpoint["optim"])
|
|
print("Successfully restore policy and optim.")
|
|
else:
|
|
print("Fail to restore policy and optim.")
|
|
|
|
# trainer
|
|
trainer = OnpolicyTrainer(
|
|
policy=policy,
|
|
train_collector=train_collector,
|
|
test_collector=test_collector,
|
|
max_epoch=args.epoch,
|
|
step_per_epoch=args.step_per_epoch,
|
|
repeat_per_collect=args.repeat_per_collect,
|
|
episode_per_test=args.test_num,
|
|
batch_size=args.batch_size,
|
|
episode_per_collect=args.episode_per_collect,
|
|
stop_fn=stop_fn,
|
|
save_best_fn=save_best_fn,
|
|
logger=logger,
|
|
resume_from_log=args.resume,
|
|
save_checkpoint_fn=save_checkpoint_fn,
|
|
)
|
|
|
|
for epoch_stat in trainer:
|
|
print(f"Epoch: {epoch_stat.epoch}")
|
|
print(epoch_stat)
|
|
# print(info)
|
|
|
|
assert stop_fn(epoch_stat.info_stat.best_reward)
|
|
|
|
if __name__ == "__main__":
|
|
pprint.pprint(epoch_stat)
|
|
# 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)
|
|
|
|
|
|
def test_ppo_resume(args: argparse.Namespace = get_args()) -> None:
|
|
args.resume = True
|
|
test_ppo(args)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_ppo()
|