import os import gym import torch import pickle import pprint import argparse import numpy as np from torch.utils.tensorboard import SummaryWriter from tianshou.policy import C51Policy from tianshou.utils import TensorboardLogger from tianshou.env import DummyVectorEnv from tianshou.utils.net.common import Net from tianshou.trainer import offpolicy_trainer from tianshou.data import Collector, VectorReplayBuffer, PrioritizedVectorReplayBuffer def get_args(): parser = argparse.ArgumentParser() parser.add_argument('--task', type=str, default='CartPole-v0') parser.add_argument('--seed', type=int, default=1626) 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=1e-3) parser.add_argument('--gamma', type=float, default=0.9) parser.add_argument('--num-atoms', type=int, default=51) parser.add_argument('--v-min', type=float, default=-10.) parser.add_argument('--v-max', type=float, default=10.) 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=8000) parser.add_argument('--step-per-collect', type=int, default=8) parser.add_argument('--update-per-step', type=float, default=0.125) parser.add_argument('--batch-size', type=int, default=64) parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[128, 128, 128, 128]) parser.add_argument('--training-num', type=int, default=8) 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('--resume', action="store_true") parser.add_argument( '--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu') parser.add_argument("--save-interval", type=int, default=4) args = parser.parse_known_args()[0] return args def test_c51(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 # 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 net = Net(args.state_shape, args.action_shape, hidden_sizes=args.hidden_sizes, device=args.device, softmax=True, num_atoms=args.num_atoms) optim = torch.optim.Adam(net.parameters(), lr=args.lr) policy = C51Policy( net, optim, args.gamma, args.num_atoms, args.v_min, args.v_max, 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, 'c51') writer = SummaryWriter(log_path) logger = TensorboardLogger(writer, save_interval=args.save_interval) def save_fn(policy): torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth')) def stop_fn(mean_rewards): return mean_rewards >= env.spec.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) def save_checkpoint_fn(epoch, env_step, gradient_step): # see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html torch.save({ 'model': policy.state_dict(), 'optim': optim.state_dict(), }, os.path.join(log_path, 'checkpoint.pth')) pickle.dump(train_collector.buffer, open(os.path.join(log_path, 'train_buffer.pkl'), "wb")) if args.resume: # load from existing checkpoint print(f"Loading agent under {log_path}") ckpt_path = os.path.join(log_path, 'checkpoint.pth') if os.path.exists(ckpt_path): checkpoint = torch.load(ckpt_path, map_location=args.device) policy.load_state_dict(checkpoint['model']) policy.optim.load_state_dict(checkpoint['optim']) print("Successfully restore policy and optim.") else: print("Fail to restore policy and optim.") buffer_path = os.path.join(log_path, 'train_buffer.pkl') if os.path.exists(buffer_path): train_collector.buffer = pickle.load(open(buffer_path, "rb")) print("Successfully restore buffer.") else: print("Fail to restore buffer.") # 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, update_per_step=args.update_per_step, train_fn=train_fn, test_fn=test_fn, stop_fn=stop_fn, save_fn=save_fn, logger=logger, resume_from_log=args.resume, save_checkpoint_fn=save_checkpoint_fn) 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_c51_resume(args=get_args()): args.resume = True test_c51(args) def test_pc51(args=get_args()): args.prioritized_replay = True args.gamma = .95 args.seed = 1 test_c51(args) if __name__ == '__main__': test_c51(get_args())