import argparse import os import pprint import gymnasium as gym import numpy as np import pytest import torch from gymnasium.spaces import Box from torch.utils.tensorboard import SummaryWriter from tianshou.data import Collector, VectorReplayBuffer from tianshou.policy import A2CPolicy, ImitationPolicy from tianshou.trainer import OffpolicyTrainer, OnpolicyTrainer from tianshou.utils import TensorboardLogger from tianshou.utils.net.common import ActorCritic, Net from tianshou.utils.net.discrete import Actor, Critic try: import envpool except ImportError: envpool = None def get_args(): parser = argparse.ArgumentParser() parser.add_argument("--task", type=str, default="CartPole-v0") parser.add_argument("--reward-threshold", type=float, default=None) parser.add_argument("--seed", type=int, default=1) parser.add_argument("--buffer-size", type=int, default=20000) parser.add_argument("--lr", type=float, default=1e-3) parser.add_argument("--il-lr", type=float, default=1e-3) parser.add_argument("--gamma", type=float, default=0.9) parser.add_argument("--epoch", type=int, default=10) parser.add_argument("--step-per-epoch", type=int, default=50000) parser.add_argument("--il-step-per-epoch", type=int, default=1000) parser.add_argument("--episode-per-collect", type=int, default=16) parser.add_argument("--step-per-collect", type=int, default=16) parser.add_argument("--update-per-step", type=float, default=1 / 16) parser.add_argument("--repeat-per-collect", type=int, default=1) parser.add_argument("--batch-size", type=int, default=64) parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[64, 64]) parser.add_argument("--imitation-hidden-sizes", type=int, nargs="*", default=[128]) parser.add_argument("--training-num", type=int, default=16) parser.add_argument("--test-num", type=int, default=100) parser.add_argument("--logdir", type=str, default="log") parser.add_argument("--render", type=float, default=0.0) parser.add_argument( "--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu", ) # a2c special parser.add_argument("--vf-coef", type=float, default=0.5) parser.add_argument("--ent-coef", type=float, default=0.0) parser.add_argument("--max-grad-norm", type=float, default=None) parser.add_argument("--gae-lambda", type=float, default=1.0) parser.add_argument("--rew-norm", action="store_true", default=False) return parser.parse_known_args()[0] @pytest.mark.skipif(envpool is None, reason="EnvPool doesn't support this platform") def test_a2c_with_il(args=get_args()): # if you want to use python vector env, please refer to other test scripts train_envs = env = envpool.make( args.task, env_type="gymnasium", num_envs=args.training_num, seed=args.seed, ) test_envs = envpool.make( args.task, env_type="gymnasium", num_envs=args.test_num, seed=args.seed, ) args.state_shape = env.observation_space.shape or env.observation_space.n args.action_shape = env.action_space.shape or env.action_space.n if args.reward_threshold is None: default_reward_threshold = {"CartPole-v0": 195} args.reward_threshold = default_reward_threshold.get(args.task, env.spec.reward_threshold) # seed np.random.seed(args.seed) torch.manual_seed(args.seed) # model net = Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device) actor = Actor(net, args.action_shape, device=args.device).to(args.device) critic = Critic(net, device=args.device).to(args.device) optim = torch.optim.Adam(ActorCritic(actor, critic).parameters(), lr=args.lr) dist = torch.distributions.Categorical policy = A2CPolicy( actor, critic, optim, dist, action_scaling=isinstance(env.action_space, Box), discount_factor=args.gamma, gae_lambda=args.gae_lambda, vf_coef=args.vf_coef, ent_coef=args.ent_coef, max_grad_norm=args.max_grad_norm, reward_normalization=args.rew_norm, action_space=env.action_space, ) # collector train_collector = Collector( policy, train_envs, VectorReplayBuffer(args.buffer_size, len(train_envs)), ) test_collector = Collector(policy, test_envs) # log log_path = os.path.join(args.logdir, args.task, "a2c") writer = SummaryWriter(log_path) logger = TensorboardLogger(writer) def save_best_fn(policy): torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth")) def stop_fn(mean_rewards): return mean_rewards >= args.reward_threshold # trainer result = OnpolicyTrainer( policy=policy, train_collector=train_collector, test_collector=test_collector, max_epoch=args.epoch, step_per_epoch=args.step_per_epoch, repeat_per_collect=args.repeat_per_collect, episode_per_test=args.test_num, batch_size=args.batch_size, episode_per_collect=args.episode_per_collect, stop_fn=stop_fn, save_best_fn=save_best_fn, logger=logger, ).run() assert stop_fn(result["best_reward"]) if __name__ == "__main__": pprint.pprint(result) # Let's watch its performance! env = gym.make(args.task) policy.eval() collector = Collector(policy, env) result = collector.collect(n_episode=1, render=args.render) rews, lens = result["rews"], result["lens"] print(f"Final reward: {rews.mean()}, length: {lens.mean()}") policy.eval() # here we define an imitation collector with a trivial policy # if args.task == 'CartPole-v0': # env.spec.reward_threshold = 190 # lower the goal net = Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device) net = Actor(net, args.action_shape, device=args.device).to(args.device) optim = torch.optim.Adam(net.parameters(), lr=args.il_lr) il_policy = ImitationPolicy(net, optim, action_space=env.action_space) il_test_collector = Collector( il_policy, envpool.make(args.task, env_type="gymnasium", num_envs=args.test_num, seed=args.seed), ) train_collector.reset() result = OffpolicyTrainer( policy=il_policy, train_collector=train_collector, test_collector=il_test_collector, max_epoch=args.epoch, step_per_epoch=args.il_step_per_epoch, step_per_collect=args.step_per_collect, episode_per_test=args.test_num, batch_size=args.batch_size, stop_fn=stop_fn, save_best_fn=save_best_fn, logger=logger, ).run() assert stop_fn(result["best_reward"]) if __name__ == "__main__": pprint.pprint(result) # Let's watch its performance! env = gym.make(args.task) il_policy.eval() collector = Collector(il_policy, env) result = collector.collect(n_episode=1, render=args.render) rews, lens = result["rews"], result["lens"] print(f"Final reward: {rews.mean()}, length: {lens.mean()}") if __name__ == "__main__": test_a2c_with_il()