#!/usr/bin/env python3 import argparse import datetime import os import pickle import pprint import numpy as np import torch from torch.utils.tensorboard import SummaryWriter from examples.atari.atari_network import QRDQN from examples.atari.atari_wrapper import make_atari_env from examples.offline.utils import load_buffer from tianshou.data import Collector, VectorReplayBuffer from tianshou.policy import DiscreteCQLPolicy from tianshou.trainer import OfflineTrainer from tianshou.utils import TensorboardLogger, WandbLogger def get_args(): parser = argparse.ArgumentParser() parser.add_argument("--task", type=str, default="PongNoFrameskip-v4") parser.add_argument("--seed", type=int, default=1626) parser.add_argument("--eps-test", type=float, default=0.001) parser.add_argument("--lr", type=float, default=0.0001) parser.add_argument("--gamma", type=float, default=0.99) parser.add_argument("--num-quantiles", type=int, default=200) parser.add_argument("--n-step", type=int, default=1) parser.add_argument("--target-update-freq", type=int, default=500) parser.add_argument("--min-q-weight", type=float, default=10.) parser.add_argument("--epoch", type=int, default=100) parser.add_argument("--update-per-epoch", type=int, default=10000) parser.add_argument("--batch-size", type=int, default=32) parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[512]) parser.add_argument("--test-num", type=int, default=10) parser.add_argument("--frames-stack", type=int, default=4) parser.add_argument("--scale-obs", type=int, default=0) parser.add_argument("--logdir", type=str, default="log") parser.add_argument("--render", type=float, default=0.) 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="offline_atari.benchmark") parser.add_argument( "--watch", default=False, action="store_true", help="watch the play of pre-trained policy only" ) parser.add_argument("--log-interval", type=int, default=100) parser.add_argument( "--load-buffer-name", type=str, default="./expert_DQN_PongNoFrameskip-v4.hdf5" ) parser.add_argument( "--buffer-from-rl-unplugged", action="store_true", default=False ) 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_discrete_cql(args=get_args()): # envs env, _, test_envs = make_atari_env( args.task, args.seed, 1, 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) # model net = QRDQN(*args.state_shape, args.action_shape, args.num_quantiles, args.device) optim = torch.optim.Adam(net.parameters(), lr=args.lr) # define policy policy = DiscreteCQLPolicy( net, optim, args.gamma, args.num_quantiles, args.n_step, args.target_update_freq, min_q_weight=args.min_q_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) # buffer if args.buffer_from_rl_unplugged: buffer = load_buffer(args.load_buffer_name) else: assert os.path.exists(args.load_buffer_name), \ "Please run atari_dqn.py first to get expert's data buffer." if args.load_buffer_name.endswith(".pkl"): buffer = pickle.load(open(args.load_buffer_name, "rb")) elif args.load_buffer_name.endswith(".hdf5"): buffer = VectorReplayBuffer.load_hdf5(args.load_buffer_name) else: print(f"Unknown buffer format: {args.load_buffer_name}") exit(0) print("Replay buffer size:", len(buffer), flush=True) # collector test_collector = Collector(policy, test_envs, exploration_noise=True) # log now = datetime.datetime.now().strftime("%y%m%d-%H%M%S") args.algo_name = "cql" 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): return False # watch agent's performance def watch(): print("Setup test envs ...") policy.eval() policy.set_eps(args.eps_test) test_envs.seed(args.seed) print("Testing agent ...") test_collector.reset() result = test_collector.collect(n_episode=args.test_num, render=args.render) pprint.pprint(result) rew = result["rews"].mean() print(f'Mean reward (over {result["n/ep"]} episodes): {rew}') if args.watch: watch() exit(0) result = OfflineTrainer( policy=policy, buffer=buffer, test_collector=test_collector, max_epoch=args.epoch, step_per_epoch=args.update_per_epoch, episode_per_test=args.test_num, batch_size=args.batch_size, stop_fn=stop_fn, save_best_fn=save_best_fn, logger=logger, ).run() pprint.pprint(result) watch() if __name__ == "__main__": test_discrete_cql(get_args())