6c6c872523
Changes: - Disclaimer in README - Replaced all occurences of Gym with Gymnasium - Removed code that is now dead since we no longer need to support the old step API - Updated type hints to only allow new step API - Increased required version of envpool to support Gymnasium - Increased required version of PettingZoo to support Gymnasium - Updated `PettingZooEnv` to only use the new step API, removed hack to also support old API - I had to add some `# type: ignore` comments, due to new type hinting in Gymnasium. I'm not that familiar with type hinting but I believe that the issue is on the Gymnasium side and we are looking into it. - Had to update `MyTestEnv` to support `options` kwarg - Skip NNI tests because they still use OpenAI Gym - Also allow `PettingZooEnv` in vector environment - Updated doc page about ReplayBuffer to also talk about terminated and truncated flags. Still need to do: - Update the Jupyter notebooks in docs - Check the entire code base for more dead code (from compatibility stuff) - Check the reset functions of all environments/wrappers in code base to make sure they use the `options` kwarg - Someone might want to check test_env_finite.py - Is it okay to allow `PettingZooEnv` in vector environments? Might need to update docs?
241 lines
7.7 KiB
Python
241 lines
7.7 KiB
Python
#!/usr/bin/env python3
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import argparse
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import datetime
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import os
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import pprint
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import gymnasium as gym
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import numpy as np
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import torch
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from torch.utils.tensorboard import SummaryWriter
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from examples.offline.utils import load_buffer_d4rl
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from tianshou.data import Collector
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from tianshou.env import SubprocVectorEnv
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from tianshou.policy import BCQPolicy
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from tianshou.trainer import offline_trainer
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from tianshou.utils import TensorboardLogger, WandbLogger
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from tianshou.utils.net.common import MLP, Net
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from tianshou.utils.net.continuous import VAE, Critic, Perturbation
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("--task", type=str, default="HalfCheetah-v2")
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parser.add_argument("--seed", type=int, default=0)
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parser.add_argument(
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"--expert-data-task", type=str, default="halfcheetah-expert-v2"
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)
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parser.add_argument("--buffer-size", type=int, default=1000000)
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parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[256, 256])
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parser.add_argument("--actor-lr", type=float, default=1e-3)
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parser.add_argument("--critic-lr", type=float, default=1e-3)
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parser.add_argument("--start-timesteps", type=int, default=10000)
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parser.add_argument("--epoch", type=int, default=200)
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parser.add_argument("--step-per-epoch", type=int, default=5000)
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parser.add_argument("--n-step", type=int, default=3)
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parser.add_argument("--batch-size", type=int, default=256)
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parser.add_argument("--test-num", type=int, default=10)
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parser.add_argument("--logdir", type=str, default="log")
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parser.add_argument("--render", type=float, default=1 / 35)
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parser.add_argument("--vae-hidden-sizes", type=int, nargs="*", default=[512, 512])
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# default to 2 * action_dim
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parser.add_argument("--latent-dim", type=int)
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parser.add_argument("--gamma", default=0.99)
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parser.add_argument("--tau", default=0.005)
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# Weighting for Clipped Double Q-learning in BCQ
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parser.add_argument("--lmbda", default=0.75)
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# Max perturbation hyper-parameter for BCQ
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parser.add_argument("--phi", default=0.05)
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parser.add_argument(
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"--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu"
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)
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parser.add_argument("--resume-path", type=str, default=None)
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parser.add_argument("--resume-id", type=str, default=None)
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parser.add_argument(
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"--logger",
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type=str,
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default="tensorboard",
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choices=["tensorboard", "wandb"],
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)
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parser.add_argument("--wandb-project", type=str, default="offline_d4rl.benchmark")
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parser.add_argument(
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"--watch",
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default=False,
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action="store_true",
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help="watch the play of pre-trained policy only",
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)
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return parser.parse_args()
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def test_bcq():
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args = get_args()
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env = gym.make(args.task)
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args.state_shape = env.observation_space.shape or env.observation_space.n
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args.action_shape = env.action_space.shape or env.action_space.n
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args.max_action = env.action_space.high[0] # float
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print("device:", args.device)
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print("Observations shape:", args.state_shape)
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print("Actions shape:", args.action_shape)
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print("Action range:", np.min(env.action_space.low), np.max(env.action_space.high))
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args.state_dim = args.state_shape[0]
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args.action_dim = args.action_shape[0]
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print("Max_action", args.max_action)
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# test_envs = gym.make(args.task)
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test_envs = SubprocVectorEnv(
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[lambda: gym.make(args.task) for _ in range(args.test_num)]
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)
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# seed
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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test_envs.seed(args.seed)
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# model
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# perturbation network
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net_a = MLP(
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input_dim=args.state_dim + args.action_dim,
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output_dim=args.action_dim,
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hidden_sizes=args.hidden_sizes,
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device=args.device,
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)
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actor = Perturbation(
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net_a, max_action=args.max_action, device=args.device, phi=args.phi
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).to(args.device)
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actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
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net_c1 = Net(
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args.state_shape,
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args.action_shape,
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hidden_sizes=args.hidden_sizes,
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concat=True,
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device=args.device,
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)
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net_c2 = Net(
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args.state_shape,
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args.action_shape,
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hidden_sizes=args.hidden_sizes,
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concat=True,
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device=args.device,
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)
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critic1 = Critic(net_c1, device=args.device).to(args.device)
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critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
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critic2 = Critic(net_c2, device=args.device).to(args.device)
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critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
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# vae
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# output_dim = 0, so the last Module in the encoder is ReLU
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vae_encoder = MLP(
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input_dim=args.state_dim + args.action_dim,
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hidden_sizes=args.vae_hidden_sizes,
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device=args.device,
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)
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if not args.latent_dim:
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args.latent_dim = args.action_dim * 2
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vae_decoder = MLP(
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input_dim=args.state_dim + args.latent_dim,
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output_dim=args.action_dim,
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hidden_sizes=args.vae_hidden_sizes,
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device=args.device,
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)
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vae = VAE(
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vae_encoder,
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vae_decoder,
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hidden_dim=args.vae_hidden_sizes[-1],
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latent_dim=args.latent_dim,
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max_action=args.max_action,
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device=args.device,
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).to(args.device)
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vae_optim = torch.optim.Adam(vae.parameters())
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policy = BCQPolicy(
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actor,
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actor_optim,
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critic1,
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critic1_optim,
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critic2,
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critic2_optim,
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vae,
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vae_optim,
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device=args.device,
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gamma=args.gamma,
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tau=args.tau,
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lmbda=args.lmbda,
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)
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# load a previous policy
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if args.resume_path:
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policy.load_state_dict(torch.load(args.resume_path, map_location=args.device))
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print("Loaded agent from: ", args.resume_path)
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# collector
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test_collector = Collector(policy, test_envs)
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# log
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now = datetime.datetime.now().strftime("%y%m%d-%H%M%S")
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args.algo_name = "bcq"
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log_name = os.path.join(args.task, args.algo_name, str(args.seed), now)
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log_path = os.path.join(args.logdir, log_name)
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# logger
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if args.logger == "wandb":
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logger = WandbLogger(
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save_interval=1,
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name=log_name.replace(os.path.sep, "__"),
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run_id=args.resume_id,
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config=args,
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project=args.wandb_project,
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)
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writer = SummaryWriter(log_path)
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writer.add_text("args", str(args))
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if args.logger == "tensorboard":
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logger = TensorboardLogger(writer)
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else: # wandb
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logger.load(writer)
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def save_best_fn(policy):
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torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))
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def watch():
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if args.resume_path is None:
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args.resume_path = os.path.join(log_path, "policy.pth")
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policy.load_state_dict(
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torch.load(args.resume_path, map_location=torch.device("cpu"))
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)
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policy.eval()
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collector = Collector(policy, env)
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collector.collect(n_episode=1, render=1 / 35)
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if not args.watch:
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replay_buffer = load_buffer_d4rl(args.expert_data_task)
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# trainer
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result = offline_trainer(
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policy,
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replay_buffer,
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test_collector,
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args.epoch,
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args.step_per_epoch,
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args.test_num,
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args.batch_size,
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save_best_fn=save_best_fn,
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logger=logger,
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)
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pprint.pprint(result)
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else:
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watch()
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# Let's watch its performance!
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policy.eval()
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test_envs.seed(args.seed)
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test_collector.reset()
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result = test_collector.collect(n_episode=args.test_num, render=args.render)
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print(f"Final reward: {result['rews'].mean()}, length: {result['lens'].mean()}")
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if __name__ == "__main__":
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test_bcq()
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