600f4bbd55
Preparation for #914 and #920 Changes formatting to ruff and black. Remove python 3.8 ## Additional Changes - Removed flake8 dependencies - Adjusted pre-commit. Now CI and Make use pre-commit, reducing the duplication of linting calls - Removed check-docstyle option (ruff is doing that) - Merged format and lint. In CI the format-lint step fails if any changes are done, so it fulfills the lint functionality. --------- Co-authored-by: Jiayi Weng <jiayi@openai.com>
207 lines
7.2 KiB
Python
Executable File
207 lines
7.2 KiB
Python
Executable File
#!/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 numpy as np
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import torch
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from mujoco_env import make_mujoco_env
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.data import Collector, ReplayBuffer, VectorReplayBuffer
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from tianshou.policy import REDQPolicy
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from tianshou.trainer import OffpolicyTrainer
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from tianshou.utils import TensorboardLogger, WandbLogger
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from tianshou.utils.net.common import EnsembleLinear, Net
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from tianshou.utils.net.continuous import ActorProb, Critic
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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="Ant-v3")
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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=[256, 256])
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parser.add_argument("--ensemble-size", type=int, default=10)
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parser.add_argument("--subset-size", type=int, default=2)
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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("--gamma", type=float, default=0.99)
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parser.add_argument("--tau", type=float, default=0.005)
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parser.add_argument("--alpha", type=float, default=0.2)
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parser.add_argument("--auto-alpha", default=False, action="store_true")
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parser.add_argument("--alpha-lr", type=float, default=3e-4)
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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("--step-per-collect", type=int, default=1)
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parser.add_argument("--update-per-step", type=int, default=20)
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parser.add_argument("--n-step", type=int, default=1)
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parser.add_argument("--batch-size", type=int, default=256)
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parser.add_argument("--target-mode", type=str, choices=("min", "mean"), default="min")
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parser.add_argument("--training-num", type=int, default=1)
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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=0.0)
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parser.add_argument(
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"--device",
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type=str,
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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="mujoco.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_redq(args=get_args()):
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env, train_envs, test_envs = make_mujoco_env(
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args.task,
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args.seed,
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args.training_num,
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args.test_num,
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obs_norm=False,
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)
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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]
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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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# seed
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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# model
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net_a = Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
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actor = ActorProb(
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net_a,
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args.action_shape,
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device=args.device,
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unbounded=True,
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conditioned_sigma=True,
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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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def linear(x, y):
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return EnsembleLinear(args.ensemble_size, x, y)
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net_c = 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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linear_layer=linear,
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)
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critics = Critic(
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net_c,
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device=args.device,
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linear_layer=linear,
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flatten_input=False,
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).to(args.device)
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critics_optim = torch.optim.Adam(critics.parameters(), lr=args.critic_lr)
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if args.auto_alpha:
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target_entropy = -np.prod(env.action_space.shape)
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log_alpha = torch.zeros(1, requires_grad=True, device=args.device)
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alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr)
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args.alpha = (target_entropy, log_alpha, alpha_optim)
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policy = REDQPolicy(
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actor,
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actor_optim,
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critics,
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critics_optim,
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args.ensemble_size,
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args.subset_size,
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tau=args.tau,
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gamma=args.gamma,
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alpha=args.alpha,
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estimation_step=args.n_step,
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actor_delay=args.update_per_step,
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target_mode=args.target_mode,
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action_space=env.action_space,
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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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now = datetime.datetime.now().strftime("%y%m%d-%H%M%S")
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args.algo_name = "redq"
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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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if not args.watch:
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# trainer
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result = OffpolicyTrainer(
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policy=policy,
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train_collector=train_collector,
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test_collector=test_collector,
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max_epoch=args.epoch,
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step_per_epoch=args.step_per_epoch,
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step_per_collect=args.step_per_collect,
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episode_per_test=args.test_num,
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batch_size=args.batch_size,
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save_best_fn=save_best_fn,
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logger=logger,
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update_per_step=args.update_per_step,
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test_in_train=False,
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).run()
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pprint.pprint(result)
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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_redq()
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