import argparse import datetime import os import pprint import sys import numpy as np import torch from atari_network import DQN from atari_wrapper import make_atari_env from examples.common import logger_factory from tianshou.data import Collector, VectorReplayBuffer from tianshou.policy import DiscreteSACPolicy, ICMPolicy from tianshou.policy.base import BasePolicy from tianshou.trainer import OffpolicyTrainer from tianshou.utils.net.discrete import Actor, Critic, IntrinsicCuriosityModule def get_args() -> argparse.Namespace: parser = argparse.ArgumentParser() parser.add_argument("--task", type=str, default="PongNoFrameskip-v4") parser.add_argument("--seed", type=int, default=4213) parser.add_argument("--scale-obs", type=int, default=0) parser.add_argument("--buffer-size", type=int, default=100000) parser.add_argument("--actor-lr", type=float, default=1e-5) parser.add_argument("--critic-lr", type=float, default=1e-5) parser.add_argument("--gamma", type=float, default=0.99) parser.add_argument("--n-step", type=int, default=3) parser.add_argument("--tau", type=float, default=0.005) parser.add_argument("--alpha", type=float, default=0.05) parser.add_argument("--auto-alpha", action="store_true", default=False) parser.add_argument("--alpha-lr", type=float, default=3e-4) parser.add_argument("--epoch", type=int, default=100) parser.add_argument("--step-per-epoch", type=int, default=100000) 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-size", type=int, default=512) parser.add_argument("--training-num", type=int, default=10) parser.add_argument("--test-num", type=int, default=10) parser.add_argument("--rew-norm", type=int, default=False) 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", ) parser.add_argument("--frames-stack", type=int, default=4) parser.add_argument("--resume-path", type=str, default=None) parser.add_argument("--resume-id", type=str, default=None) parser.add_argument( "--logger", type=str, default="tensorboard", choices=["tensorboard", "wandb"], ) parser.add_argument("--wandb-project", type=str, default="atari.benchmark") parser.add_argument( "--watch", default=False, action="store_true", help="watch the play of pre-trained policy only", ) parser.add_argument("--save-buffer-name", type=str, default=None) parser.add_argument( "--icm-lr-scale", type=float, default=0.0, help="use intrinsic curiosity module with this lr scale", ) parser.add_argument( "--icm-reward-scale", type=float, default=0.01, help="scaling factor for intrinsic curiosity reward", ) parser.add_argument( "--icm-forward-loss-weight", type=float, default=0.2, help="weight for the forward model loss in ICM", ) return parser.parse_args() def test_discrete_sac(args: argparse.Namespace = get_args()) -> None: env, train_envs, test_envs = make_atari_env( args.task, args.seed, args.training_num, args.test_num, scale=args.scale_obs, frame_stack=args.frames_stack, ) args.state_shape = env.observation_space.shape or env.observation_space.n args.action_shape = env.action_space.shape or env.action_space.n # should be N_FRAMES x H x W print("Observations shape:", args.state_shape) print("Actions shape:", args.action_shape) # seed np.random.seed(args.seed) torch.manual_seed(args.seed) # define model net = DQN( *args.state_shape, args.action_shape, device=args.device, features_only=True, output_dim=args.hidden_size, ) actor = Actor(net, args.action_shape, device=args.device, softmax_output=False) actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr) critic1 = Critic(net, last_size=args.action_shape, device=args.device) critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr) critic2 = Critic(net, last_size=args.action_shape, device=args.device) critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr) # define policy if args.auto_alpha: target_entropy = 0.98 * np.log(np.prod(args.action_shape)) log_alpha = torch.zeros(1, requires_grad=True, device=args.device) alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr) args.alpha = (target_entropy, log_alpha, alpha_optim) policy: DiscreteSACPolicy = DiscreteSACPolicy( actor=actor, actor_optim=actor_optim, critic=critic1, critic_optim=critic1_optim, critic2=critic2, critic2_optim=critic2_optim, action_space=env.action_space, tau=args.tau, gamma=args.gamma, alpha=args.alpha, estimation_step=args.n_step, ).to(args.device) if args.icm_lr_scale > 0: feature_net = DQN(*args.state_shape, args.action_shape, args.device, features_only=True) action_dim = np.prod(args.action_shape) feature_dim = feature_net.output_dim icm_net = IntrinsicCuriosityModule( feature_net.net, feature_dim, action_dim, hidden_sizes=[args.hidden_size], device=args.device, ) icm_optim = torch.optim.Adam(icm_net.parameters(), lr=args.actor_lr) policy = ICMPolicy( policy=policy, model=icm_net, optim=icm_optim, action_space=env.action_space, lr_scale=args.icm_lr_scale, reward_scale=args.icm_reward_scale, forward_loss_weight=args.icm_forward_loss_weight, ).to(args.device) # load a previous policy if args.resume_path: policy.load_state_dict(torch.load(args.resume_path, map_location=args.device)) print("Loaded agent from: ", args.resume_path) # replay buffer: `save_last_obs` and `stack_num` can be removed together # when you have enough RAM buffer = VectorReplayBuffer( args.buffer_size, buffer_num=len(train_envs), ignore_obs_next=True, save_only_last_obs=True, stack_num=args.frames_stack, ) # collector train_collector = Collector(policy, train_envs, buffer, exploration_noise=True) test_collector = Collector(policy, test_envs, exploration_noise=True) # log now = datetime.datetime.now().strftime("%y%m%d-%H%M%S") args.algo_name = "discrete_sac_icm" if args.icm_lr_scale > 0 else "discrete_sac" log_name = os.path.join(args.task, args.algo_name, str(args.seed), now) log_path = os.path.join(args.logdir, log_name) # logger if args.logger == "wandb": logger_factory.logger_type = "wandb" logger_factory.wandb_project = args.wandb_project else: logger_factory.logger_type = "tensorboard" logger = logger_factory.create_logger( log_dir=log_path, experiment_name=log_name, run_id=args.resume_id, config_dict=vars(args), ) def save_best_fn(policy: BasePolicy) -> None: torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth")) def stop_fn(mean_rewards: float) -> bool: if env.spec.reward_threshold: return mean_rewards >= env.spec.reward_threshold if "Pong" in args.task: return mean_rewards >= 20 return False def save_checkpoint_fn(epoch: int, env_step: int, gradient_step: int) -> str: # see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html ckpt_path = os.path.join(log_path, "checkpoint.pth") torch.save({"model": policy.state_dict()}, ckpt_path) return ckpt_path # watch agent's performance def watch() -> None: print("Setup test envs ...") policy.eval() test_envs.seed(args.seed) if args.save_buffer_name: print(f"Generate buffer with size {args.buffer_size}") buffer = VectorReplayBuffer( args.buffer_size, buffer_num=len(test_envs), ignore_obs_next=True, save_only_last_obs=True, stack_num=args.frames_stack, ) collector = Collector(policy, test_envs, buffer, exploration_noise=True) result = collector.collect(n_step=args.buffer_size) print(f"Save buffer into {args.save_buffer_name}") # Unfortunately, pickle will cause oom with 1M buffer size buffer.save_hdf5(args.save_buffer_name) else: print("Testing agent ...") test_collector.reset() result = test_collector.collect(n_episode=args.test_num, render=args.render) rew = result.returns_stat.mean print(f"Mean reward (over {result['n/ep']} episodes): {rew}") if args.watch: watch() sys.exit(0) # test train_collector and start filling replay buffer train_collector.collect(n_step=args.batch_size * args.training_num) # trainer result = OffpolicyTrainer( policy=policy, train_collector=train_collector, test_collector=test_collector, max_epoch=args.epoch, step_per_epoch=args.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, update_per_step=args.update_per_step, test_in_train=False, resume_from_log=args.resume_id is not None, save_checkpoint_fn=save_checkpoint_fn, ).run() pprint.pprint(result) watch() if __name__ == "__main__": test_discrete_sac(get_args())