eb0215cf76
Improves typing in examples and tests, towards mypy passing there. Introduces the SpaceInfo utility
241 lines
8.1 KiB
Python
241 lines
8.1 KiB
Python
#!/usr/bin/env python3
|
|
|
|
import argparse
|
|
import datetime
|
|
import os
|
|
import pprint
|
|
|
|
import gymnasium as gym
|
|
import numpy as np
|
|
import torch
|
|
from torch.utils.tensorboard import SummaryWriter
|
|
|
|
from examples.offline.utils import load_buffer_d4rl
|
|
from tianshou.data import Collector
|
|
from tianshou.env import SubprocVectorEnv
|
|
from tianshou.policy import BCQPolicy
|
|
from tianshou.policy.base import BasePolicy
|
|
from tianshou.trainer import OfflineTrainer
|
|
from tianshou.utils import TensorboardLogger, WandbLogger
|
|
from tianshou.utils.net.common import MLP, Net
|
|
from tianshou.utils.net.continuous import VAE, Critic, Perturbation
|
|
from tianshou.utils.space_info import SpaceInfo
|
|
|
|
|
|
def get_args() -> argparse.Namespace:
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--task", type=str, default="HalfCheetah-v2")
|
|
parser.add_argument("--seed", type=int, default=0)
|
|
parser.add_argument("--expert-data-task", type=str, default="halfcheetah-expert-v2")
|
|
parser.add_argument("--buffer-size", type=int, default=1000000)
|
|
parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[256, 256])
|
|
parser.add_argument("--actor-lr", type=float, default=1e-3)
|
|
parser.add_argument("--critic-lr", type=float, default=1e-3)
|
|
parser.add_argument("--start-timesteps", type=int, default=10000)
|
|
parser.add_argument("--epoch", type=int, default=200)
|
|
parser.add_argument("--step-per-epoch", type=int, default=5000)
|
|
parser.add_argument("--n-step", type=int, default=3)
|
|
parser.add_argument("--batch-size", type=int, default=256)
|
|
parser.add_argument("--test-num", type=int, default=10)
|
|
parser.add_argument("--logdir", type=str, default="log")
|
|
parser.add_argument("--render", type=float, default=1 / 35)
|
|
|
|
parser.add_argument("--vae-hidden-sizes", type=int, nargs="*", default=[512, 512])
|
|
# default to 2 * action_dim
|
|
parser.add_argument("--latent-dim", type=int)
|
|
parser.add_argument("--gamma", default=0.99)
|
|
parser.add_argument("--tau", default=0.005)
|
|
# Weighting for Clipped Double Q-learning in BCQ
|
|
parser.add_argument("--lmbda", default=0.75)
|
|
# Max perturbation hyper-parameter for BCQ
|
|
parser.add_argument("--phi", default=0.05)
|
|
parser.add_argument(
|
|
"--device",
|
|
type=str,
|
|
default="cuda" if torch.cuda.is_available() else "cpu",
|
|
)
|
|
parser.add_argument("--resume-path", type=str, default=None)
|
|
parser.add_argument("--resume-id", type=str, default=None)
|
|
parser.add_argument(
|
|
"--logger",
|
|
type=str,
|
|
default="tensorboard",
|
|
choices=["tensorboard", "wandb"],
|
|
)
|
|
parser.add_argument("--wandb-project", type=str, default="offline_d4rl.benchmark")
|
|
parser.add_argument(
|
|
"--watch",
|
|
default=False,
|
|
action="store_true",
|
|
help="watch the play of pre-trained policy only",
|
|
)
|
|
return parser.parse_args()
|
|
|
|
|
|
def test_bcq() -> None:
|
|
args = get_args()
|
|
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
|
|
print("device:", args.device)
|
|
print("Observations shape:", args.state_shape)
|
|
print("Actions shape:", args.action_shape)
|
|
print("Action range:", args.min_action, args.max_action)
|
|
|
|
args.state_dim = space_info.observation_info.obs_dim
|
|
args.action_dim = space_info.action_info.action_dim
|
|
print("Max_action", args.max_action)
|
|
|
|
# test_envs = gym.make(args.task)
|
|
test_envs = SubprocVectorEnv([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
|
|
# perturbation network
|
|
net_a = MLP(
|
|
input_dim=args.state_dim + args.action_dim,
|
|
output_dim=args.action_dim,
|
|
hidden_sizes=args.hidden_sizes,
|
|
device=args.device,
|
|
)
|
|
actor = Perturbation(net_a, max_action=args.max_action, device=args.device, phi=args.phi).to(
|
|
args.device,
|
|
)
|
|
actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
|
|
|
|
net_c1 = Net(
|
|
args.state_shape,
|
|
args.action_shape,
|
|
hidden_sizes=args.hidden_sizes,
|
|
concat=True,
|
|
device=args.device,
|
|
)
|
|
net_c2 = Net(
|
|
args.state_shape,
|
|
args.action_shape,
|
|
hidden_sizes=args.hidden_sizes,
|
|
concat=True,
|
|
device=args.device,
|
|
)
|
|
critic1 = Critic(net_c1, device=args.device).to(args.device)
|
|
critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
|
|
critic2 = Critic(net_c2, device=args.device).to(args.device)
|
|
critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
|
|
|
|
# vae
|
|
# output_dim = 0, so the last Module in the encoder is ReLU
|
|
vae_encoder = MLP(
|
|
input_dim=args.state_dim + args.action_dim,
|
|
hidden_sizes=args.vae_hidden_sizes,
|
|
device=args.device,
|
|
)
|
|
if not args.latent_dim:
|
|
args.latent_dim = args.action_dim * 2
|
|
vae_decoder = MLP(
|
|
input_dim=args.state_dim + args.latent_dim,
|
|
output_dim=args.action_dim,
|
|
hidden_sizes=args.vae_hidden_sizes,
|
|
device=args.device,
|
|
)
|
|
vae = VAE(
|
|
vae_encoder,
|
|
vae_decoder,
|
|
hidden_dim=args.vae_hidden_sizes[-1],
|
|
latent_dim=args.latent_dim,
|
|
max_action=args.max_action,
|
|
device=args.device,
|
|
).to(args.device)
|
|
vae_optim = torch.optim.Adam(vae.parameters())
|
|
|
|
policy: BCQPolicy = BCQPolicy(
|
|
actor_perturbation=actor,
|
|
actor_perturbation_optim=actor_optim,
|
|
critic=critic1,
|
|
critic_optim=critic1_optim,
|
|
action_space=env.action_space,
|
|
critic2=critic2,
|
|
critic2_optim=critic2_optim,
|
|
vae=vae,
|
|
vae_optim=vae_optim,
|
|
device=args.device,
|
|
gamma=args.gamma,
|
|
tau=args.tau,
|
|
lmbda=args.lmbda,
|
|
)
|
|
|
|
# load a previous policy
|
|
if args.resume_path:
|
|
policy.load_state_dict(torch.load(args.resume_path, map_location=args.device))
|
|
print("Loaded agent from: ", args.resume_path)
|
|
|
|
# collector
|
|
test_collector = Collector(policy, test_envs)
|
|
|
|
# log
|
|
now = datetime.datetime.now().strftime("%y%m%d-%H%M%S")
|
|
args.algo_name = "bcq"
|
|
log_name = os.path.join(args.task, args.algo_name, str(args.seed), now)
|
|
log_path = os.path.join(args.logdir, log_name)
|
|
|
|
# logger
|
|
writer = SummaryWriter(log_path)
|
|
writer.add_text("args", str(args))
|
|
logger: WandbLogger | TensorboardLogger
|
|
if args.logger == "tensorboard":
|
|
logger = TensorboardLogger(writer)
|
|
else:
|
|
logger = WandbLogger(
|
|
save_interval=1,
|
|
name=log_name.replace(os.path.sep, "__"),
|
|
run_id=args.resume_id,
|
|
config=args,
|
|
project=args.wandb_project,
|
|
)
|
|
logger.load(writer)
|
|
|
|
def save_best_fn(policy: BasePolicy) -> None:
|
|
torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))
|
|
|
|
def watch() -> None:
|
|
if args.resume_path is None:
|
|
args.resume_path = os.path.join(log_path, "policy.pth")
|
|
|
|
policy.load_state_dict(torch.load(args.resume_path, map_location=torch.device("cpu")))
|
|
policy.eval()
|
|
collector = Collector(policy, env)
|
|
collector.collect(n_episode=1, render=1 / 35)
|
|
|
|
if not args.watch:
|
|
replay_buffer = load_buffer_d4rl(args.expert_data_task)
|
|
# trainer
|
|
result = OfflineTrainer(
|
|
policy=policy,
|
|
buffer=replay_buffer,
|
|
test_collector=test_collector,
|
|
max_epoch=args.epoch,
|
|
step_per_epoch=args.step_per_epoch,
|
|
episode_per_test=args.test_num,
|
|
batch_size=args.batch_size,
|
|
save_best_fn=save_best_fn,
|
|
logger=logger,
|
|
).run()
|
|
pprint.pprint(result)
|
|
else:
|
|
watch()
|
|
|
|
# Let's watch its performance!
|
|
policy.eval()
|
|
test_envs.seed(args.seed)
|
|
test_collector.reset()
|
|
collector_stats = test_collector.collect(n_episode=args.test_num, render=args.render)
|
|
print(collector_stats)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test_bcq()
|