import argparse import os import pprint import gym import numpy as np import torch from torch.utils.tensorboard import SummaryWriter from tianshou.data import Collector, PrioritizedVectorReplayBuffer, VectorReplayBuffer from tianshou.env import DummyVectorEnv from tianshou.policy import IQNPolicy from tianshou.trainer import offpolicy_trainer from tianshou.utils import TensorboardLogger from tianshou.utils.net.common import Net from tianshou.utils.net.discrete import ImplicitQuantileNetwork 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=0) parser.add_argument('--eps-test', type=float, default=0.05) parser.add_argument('--eps-train', type=float, default=0.1) parser.add_argument('--buffer-size', type=int, default=20000) parser.add_argument('--lr', type=float, default=3e-3) parser.add_argument('--gamma', type=float, default=0.9) parser.add_argument('--sample-size', type=int, default=32) parser.add_argument('--online-sample-size', type=int, default=8) parser.add_argument('--target-sample-size', type=int, default=8) parser.add_argument('--num-cosines', type=int, default=64) parser.add_argument('--n-step', type=int, default=3) parser.add_argument('--target-update-freq', type=int, default=320) parser.add_argument('--epoch', type=int, default=10) parser.add_argument('--step-per-epoch', type=int, default=10000) parser.add_argument('--step-per-collect', type=int, default=10) parser.add_argument('--update-per-step', type=float, default=0.1) parser.add_argument('--batch-size', type=int, default=64) parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[64, 64, 64]) parser.add_argument('--training-num', type=int, default=10) 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.) parser.add_argument('--prioritized-replay', action="store_true", default=False) parser.add_argument('--alpha', type=float, default=0.6) parser.add_argument('--beta', type=float, default=0.4) parser.add_argument( '--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu' ) args = parser.parse_known_args()[0] return args def test_iqn(args=get_args()): env = gym.make(args.task) 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 ) # train_envs = gym.make(args.task) # you can also use tianshou.env.SubprocVectorEnv train_envs = DummyVectorEnv( [lambda: gym.make(args.task) for _ in range(args.training_num)] ) # test_envs = gym.make(args.task) test_envs = DummyVectorEnv( [lambda: gym.make(args.task) for _ in range(args.test_num)] ) # seed np.random.seed(args.seed) torch.manual_seed(args.seed) train_envs.seed(args.seed) test_envs.seed(args.seed) # model feature_net = Net( args.state_shape, args.hidden_sizes[-1], hidden_sizes=args.hidden_sizes[:-1], device=args.device, softmax=False ) net = ImplicitQuantileNetwork( feature_net, args.action_shape, num_cosines=args.num_cosines, device=args.device ) optim = torch.optim.Adam(net.parameters(), lr=args.lr) policy = IQNPolicy( net, optim, args.gamma, args.sample_size, args.online_sample_size, args.target_sample_size, args.n_step, target_update_freq=args.target_update_freq ).to(args.device) # buffer if args.prioritized_replay: buf = PrioritizedVectorReplayBuffer( args.buffer_size, buffer_num=len(train_envs), alpha=args.alpha, beta=args.beta ) else: buf = VectorReplayBuffer(args.buffer_size, buffer_num=len(train_envs)) # collector train_collector = Collector(policy, train_envs, buf, exploration_noise=True) test_collector = Collector(policy, test_envs, exploration_noise=True) # policy.set_eps(1) train_collector.collect(n_step=args.batch_size * args.training_num) # log log_path = os.path.join(args.logdir, args.task, 'iqn') 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 def train_fn(epoch, env_step): # eps annnealing, just a demo if env_step <= 10000: policy.set_eps(args.eps_train) elif env_step <= 50000: eps = args.eps_train - (env_step - 10000) / \ 40000 * (0.9 * args.eps_train) policy.set_eps(eps) else: policy.set_eps(0.1 * args.eps_train) def test_fn(epoch, env_step): policy.set_eps(args.eps_test) # trainer result = offpolicy_trainer( policy, train_collector, test_collector, args.epoch, args.step_per_epoch, args.step_per_collect, args.test_num, args.batch_size, train_fn=train_fn, test_fn=test_fn, stop_fn=stop_fn, save_best_fn=save_best_fn, logger=logger, update_per_step=args.update_per_step ) assert stop_fn(result['best_reward']) if __name__ == '__main__': pprint.pprint(result) # Let's watch its performance! env = gym.make(args.task) policy.eval() policy.set_eps(args.eps_test) 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()}") def test_piqn(args=get_args()): args.prioritized_replay = True args.gamma = .95 test_iqn(args) if __name__ == '__main__': test_iqn(get_args())