5c5a3db94e
This PR implements BCQPolicy, which could be used to train an offline agent in the environment of continuous action space. An experimental result 'halfcheetah-expert-v1' is provided, which is a d4rl environment (for Offline Reinforcement Learning). Example usage is in the examples/offline/offline_bcq.py.
242 lines
8.0 KiB
Python
242 lines
8.0 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 d4rl
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import 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 tianshou.data import Batch, Collector, ReplayBuffer, VectorReplayBuffer
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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 BasicLogger
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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-expert-v1')
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parser.add_argument('--seed', type=int, default=0)
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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=[400, 300])
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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('--training-num', type=int, default=10)
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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=[750, 750])
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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(
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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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# train_envs = gym.make(args.task)
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train_envs = SubprocVectorEnv(
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[lambda: gym.make(args.task) for _ in range(args.training_num)]
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)
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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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train_envs.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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if args.training_num > 1:
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buffer = VectorReplayBuffer(args.buffer_size, len(train_envs))
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else:
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buffer = ReplayBuffer(args.buffer_size)
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train_collector = Collector(policy, train_envs, buffer, exploration_noise=True)
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test_collector = Collector(policy, test_envs)
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train_collector.collect(n_step=args.start_timesteps, random=True)
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# log
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t0 = datetime.datetime.now().strftime("%m%d_%H%M%S")
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log_file = f'seed_{args.seed}_{t0}-{args.task.replace("-", "_")}_bcq'
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log_path = os.path.join(args.logdir, args.task, 'bcq', log_file)
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writer = SummaryWriter(log_path)
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writer.add_text("args", str(args))
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logger = BasicLogger(writer)
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def save_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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dataset = d4rl.qlearning_dataset(env)
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dataset_size = dataset['rewards'].size
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print("dataset_size", dataset_size)
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replay_buffer = ReplayBuffer(dataset_size)
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for i in range(dataset_size):
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replay_buffer.add(
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Batch(
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obs=dataset['observations'][i],
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act=dataset['actions'][i],
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rew=dataset['rewards'][i],
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done=dataset['terminals'][i],
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obs_next=dataset['next_observations'][i],
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)
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)
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print("dataset loaded")
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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_fn=save_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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