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test_discrete_bcq.py
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test_discrete_bcq.py
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import argparse
import os
import pickle
from test.offline.gather_cartpole_data import expert_file_name, gather_data
import gymnasium as gym
import numpy as np
import torch
from torch.utils.tensorboard import SummaryWriter
from tianshou.data import Collector, PrioritizedVectorReplayBuffer, VectorReplayBuffer
from tianshou.env import DummyVectorEnv
from tianshou.policy import BasePolicy, DiscreteBCQPolicy
from tianshou.trainer import OfflineTrainer
from tianshou.utils import TensorboardLogger
from tianshou.utils.net.common import ActorCritic, Net
from tianshou.utils.net.discrete import Actor
from tianshou.utils.space_info import SpaceInfo
def get_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--task", type=str, default="CartPole-v1")
parser.add_argument("--reward-threshold", type=float, default=None)
parser.add_argument("--seed", type=int, default=1626)
parser.add_argument("--eps-test", type=float, default=0.001)
parser.add_argument("--lr", type=float, default=3e-4)
parser.add_argument("--gamma", type=float, default=0.99)
parser.add_argument("--n-step", type=int, default=3)
parser.add_argument("--target-update-freq", type=int, default=320)
parser.add_argument("--unlikely-action-threshold", type=float, default=0.6)
parser.add_argument("--imitation-logits-penalty", type=float, default=0.01)
parser.add_argument("--epoch", type=int, default=5)
parser.add_argument("--update-per-epoch", type=int, default=2000)
parser.add_argument("--batch-size", type=int, default=64)
parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[64, 64])
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("--load-buffer-name", type=str, default=expert_file_name())
parser.add_argument(
"--device",
type=str,
default="cuda" if torch.cuda.is_available() else "cpu",
)
parser.add_argument("--resume", action="store_true")
parser.add_argument("--save-interval", type=int, default=4)
return parser.parse_known_args()[0]
def test_discrete_bcq(args: argparse.Namespace = get_args()) -> None:
# envs
env = gym.make(args.task)
assert isinstance(env.action_space, gym.spaces.Discrete)
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-v1": 185}
args.reward_threshold = default_reward_threshold.get(
args.task,
env.spec.reward_threshold if env.spec else None,
)
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)
test_envs.seed(args.seed)
# model
net = Net(state_shape=args.state_shape, action_shape=args.hidden_sizes[0], device=args.device)
policy_net = Actor(
net,
args.action_shape,
hidden_sizes=args.hidden_sizes,
device=args.device,
).to(args.device)
imitation_net = Actor(
net,
args.action_shape,
hidden_sizes=args.hidden_sizes,
device=args.device,
).to(args.device)
actor_critic = ActorCritic(policy_net, imitation_net)
optim = torch.optim.Adam(actor_critic.parameters(), lr=args.lr)
policy: DiscreteBCQPolicy = DiscreteBCQPolicy(
model=policy_net,
imitator=imitation_net,
optim=optim,
action_space=env.action_space,
discount_factor=args.gamma,
estimation_step=args.n_step,
target_update_freq=args.target_update_freq,
eval_eps=args.eps_test,
unlikely_action_threshold=args.unlikely_action_threshold,
imitation_logits_penalty=args.imitation_logits_penalty,
)
# buffer
buffer: VectorReplayBuffer | PrioritizedVectorReplayBuffer
if os.path.exists(args.load_buffer_name) and os.path.isfile(args.load_buffer_name):
if args.load_buffer_name.endswith(".hdf5"):
buffer = VectorReplayBuffer.load_hdf5(args.load_buffer_name)
else:
with open(args.load_buffer_name, "rb") as f:
buffer = pickle.load(f)
else:
buffer = gather_data()
# collector
test_collector = Collector(policy, test_envs, exploration_noise=True)
log_path = os.path.join(args.logdir, args.task, "discrete_bcq")
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.")
result = OfflineTrainer(
policy=policy,
buffer=buffer,
test_collector=test_collector,
max_epoch=args.epoch,
step_per_epoch=args.update_per_epoch,
episode_per_test=args.test_num,
batch_size=args.batch_size,
stop_fn=stop_fn,
save_best_fn=save_best_fn,
logger=logger,
resume_from_log=args.resume,
save_checkpoint_fn=save_checkpoint_fn,
).run()
assert stop_fn(result.best_reward)
def test_discrete_bcq_resume(args: argparse.Namespace = get_args()) -> None:
test_discrete_bcq()
args.resume = True
test_discrete_bcq(args)