import argparse import datetime import os import pprint import sys import numpy as np import torch from atari_network import DQN, layer_init, scale_obs from atari_wrapper import make_atari_env from torch.optim.lr_scheduler import LambdaLR from torch.utils.tensorboard import SummaryWriter from tianshou.data import Collector, VectorReplayBuffer from tianshou.policy import ICMPolicy, PPOPolicy from tianshou.trainer import OnpolicyTrainer from tianshou.utils import TensorboardLogger, WandbLogger from tianshou.utils.net.common import ActorCritic from tianshou.utils.net.discrete import Actor, Critic, IntrinsicCuriosityModule def get_args(): 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=1) parser.add_argument("--buffer-size", type=int, default=100000) parser.add_argument("--lr", type=float, default=2.5e-4) parser.add_argument("--gamma", type=float, default=0.99) 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=1000) parser.add_argument("--repeat-per-collect", type=int, default=4) parser.add_argument("--batch-size", type=int, default=256) 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("--vf-coef", type=float, default=0.25) parser.add_argument("--ent-coef", type=float, default=0.01) parser.add_argument("--gae-lambda", type=float, default=0.95) parser.add_argument("--lr-decay", type=int, default=True) parser.add_argument("--max-grad-norm", type=float, default=0.5) parser.add_argument("--eps-clip", type=float, default=0.1) parser.add_argument("--dual-clip", type=float, default=None) parser.add_argument("--value-clip", type=int, default=1) parser.add_argument("--norm-adv", type=int, default=1) parser.add_argument("--recompute-adv", type=int, default=0) 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_ppo(args=get_args()): env, train_envs, test_envs = make_atari_env( args.task, args.seed, args.training_num, args.test_num, scale=0, 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, layer_init=layer_init, ) if args.scale_obs: net = scale_obs(net) actor = Actor(net, args.action_shape, device=args.device, softmax_output=False) critic = Critic(net, device=args.device) optim = torch.optim.Adam(ActorCritic(actor, critic).parameters(), lr=args.lr, eps=1e-5) lr_scheduler = None if args.lr_decay: # decay learning rate to 0 linearly max_update_num = np.ceil(args.step_per_epoch / args.step_per_collect) * args.epoch lr_scheduler = LambdaLR(optim, lr_lambda=lambda epoch: 1 - epoch / max_update_num) # define policy def dist(p): return torch.distributions.Categorical(logits=p) policy = PPOPolicy( actor=actor, critic=critic, optim=optim, dist_fn=dist, discount_factor=args.gamma, gae_lambda=args.gae_lambda, max_grad_norm=args.max_grad_norm, vf_coef=args.vf_coef, ent_coef=args.ent_coef, reward_normalization=args.rew_norm, action_scaling=False, lr_scheduler=lr_scheduler, action_space=env.action_space, eps_clip=args.eps_clip, value_clip=args.value_clip, dual_clip=args.dual_clip, advantage_normalization=args.norm_adv, recompute_advantage=args.recompute_adv, ).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.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 = "ppo_icm" if args.icm_lr_scale > 0 else "ppo" 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 = WandbLogger( save_interval=1, name=log_name.replace(os.path.sep, "__"), run_id=args.resume_id, config=args, project=args.wandb_project, ) writer = SummaryWriter(log_path) writer.add_text("args", str(args)) if args.logger == "tensorboard": logger = TensorboardLogger(writer) else: # wandb logger.load(writer) def save_best_fn(policy): 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, env_step, gradient_step): # see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html ckpt_path = os.path.join(log_path, f"checkpoint_{epoch}.pth") torch.save({"model": policy.state_dict()}, ckpt_path) return ckpt_path # watch agent's performance def watch(): 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 = 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, step_per_collect=args.step_per_collect, stop_fn=stop_fn, save_best_fn=save_best_fn, logger=logger, 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_ppo(get_args())