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>
198 lines
7.3 KiB
Python
198 lines
7.3 KiB
Python
import argparse
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import os
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import numpy as np
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import pytest
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import torch
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.data import Collector, VectorReplayBuffer
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from tianshou.policy import ImitationPolicy, SACPolicy
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from tianshou.trainer import OffpolicyTrainer
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from tianshou.utils import TensorboardLogger
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from tianshou.utils.net.common import Net
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from tianshou.utils.net.continuous import Actor, ActorProb, Critic
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try:
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import envpool
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except ImportError:
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envpool = None
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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="Pendulum-v1")
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parser.add_argument("--reward-threshold", type=float, default=None)
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parser.add_argument("--seed", type=int, default=1)
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parser.add_argument("--buffer-size", type=int, default=20000)
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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("--il-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", type=int, default=1)
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parser.add_argument("--alpha-lr", type=float, default=3e-4)
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parser.add_argument("--epoch", type=int, default=5)
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parser.add_argument("--step-per-epoch", type=int, default=24000)
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parser.add_argument("--il-step-per-epoch", type=int, default=500)
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parser.add_argument("--step-per-collect", type=int, default=10)
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parser.add_argument("--update-per-step", type=float, default=0.1)
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parser.add_argument("--batch-size", type=int, default=128)
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parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[128, 128])
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parser.add_argument("--imitation-hidden-sizes", type=int, nargs="*", default=[128, 128])
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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=100)
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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("--rew-norm", action="store_true", default=False)
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parser.add_argument("--n-step", type=int, default=3)
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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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return parser.parse_known_args()[0]
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@pytest.mark.skipif(envpool is None, reason="EnvPool doesn't support this platform")
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def test_sac_with_il(args=get_args()):
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# if you want to use python vector env, please refer to other test scripts
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train_envs = env = envpool.make_gymnasium(args.task, num_envs=args.training_num, seed=args.seed)
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test_envs = envpool.make_gymnasium(args.task, num_envs=args.test_num, seed=args.seed)
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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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if args.reward_threshold is None:
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default_reward_threshold = {"Pendulum-v0": -250, "Pendulum-v1": -250}
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args.reward_threshold = default_reward_threshold.get(args.task, env.spec.reward_threshold)
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# you can also use tianshou.env.SubprocVectorEnv
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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 = Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
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actor = ActorProb(net, args.action_shape, device=args.device, unbounded=True).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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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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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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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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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 = SACPolicy(
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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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tau=args.tau,
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gamma=args.gamma,
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alpha=args.alpha,
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reward_normalization=args.rew_norm,
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estimation_step=args.n_step,
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action_space=env.action_space,
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)
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# collector
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train_collector = Collector(
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policy,
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train_envs,
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VectorReplayBuffer(args.buffer_size, len(train_envs)),
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exploration_noise=True,
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)
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test_collector = Collector(policy, test_envs)
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# train_collector.collect(n_step=args.buffer_size)
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# log
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log_path = os.path.join(args.logdir, args.task, "sac")
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writer = SummaryWriter(log_path)
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logger = TensorboardLogger(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 stop_fn(mean_rewards):
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return mean_rewards >= args.reward_threshold
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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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update_per_step=args.update_per_step,
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stop_fn=stop_fn,
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save_best_fn=save_best_fn,
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logger=logger,
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).run()
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assert stop_fn(result["best_reward"])
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# here we define an imitation collector with a trivial policy
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policy.eval()
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if args.task.startswith("Pendulum"):
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args.reward_threshold -= 50 # lower the goal
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net = Actor(
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Net(
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args.state_shape,
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hidden_sizes=args.imitation_hidden_sizes,
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device=args.device,
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),
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args.action_shape,
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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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optim = torch.optim.Adam(net.parameters(), lr=args.il_lr)
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il_policy = ImitationPolicy(
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net,
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optim,
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action_space=env.action_space,
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action_scaling=True,
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action_bound_method="clip",
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)
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il_test_collector = Collector(
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il_policy,
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envpool.make_gymnasium(args.task, num_envs=args.test_num, seed=args.seed),
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)
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train_collector.reset()
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result = OffpolicyTrainer(
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policy=il_policy,
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train_collector=train_collector,
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test_collector=il_test_collector,
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max_epoch=args.epoch,
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step_per_epoch=args.il_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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stop_fn=stop_fn,
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save_best_fn=save_best_fn,
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logger=logger,
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).run()
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assert stop_fn(result["best_reward"])
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if __name__ == "__main__":
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test_sac_with_il()
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