def test_il(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 = DQN(*args.state_shape, args.action_shape, device=args.device).to(args.device) optim = torch.optim.Adam(net.parameters(), lr=args.lr) # define policy policy = ImitationPolicy(net, optim, action_space=env.action_space) # 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 = "il" 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() 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 = offline_trainer( policy, buffer, test_collector, args.epoch, args.update_per_epoch, args.test_num, args.batch_size, stop_fn=stop_fn, save_best_fn=save_best_fn, logger=logger, ) pprint.pprint(result) watch()
def test_il(): 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 args.max_action = env.action_space.high[0] # float print("device:", args.device) print("Observations shape:", args.state_shape) print("Actions shape:", args.action_shape) print("Action range:", np.min(env.action_space.low), np.max(env.action_space.high)) args.state_dim = args.state_shape[0] args.action_dim = args.action_shape[0] print("Max_action", args.max_action) test_envs = SubprocVectorEnv( [lambda: gym.make(args.task) for _ in range(args.test_num)] ) # seed np.random.seed(args.seed) torch.manual_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, ) actor = Actor( net, action_shape=args.action_shape, max_action=args.max_action, device=args.device ).to(args.device) optim = torch.optim.Adam(actor.parameters(), lr=args.lr) policy = ImitationPolicy( actor, optim, action_space=env.action_space, action_scaling=True, action_bound_method="clip" ) # 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) # collector test_collector = Collector(policy, test_envs) # 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 watch(): if args.resume_path is None: args.resume_path = os.path.join(log_path, "policy.pth") policy.load_state_dict( torch.load(args.resume_path, map_location=torch.device("cpu")) ) policy.eval() collector = Collector(policy, env) collector.collect(n_episode=1, render=1 / 35) if not args.watch: dataset = d4rl.qlearning_dataset(gym.make(args.expert_data_task)) dataset_size = dataset["rewards"].size print("dataset_size", dataset_size) replay_buffer = ReplayBuffer(dataset_size) for i in range(dataset_size): replay_buffer.add( Batch( obs=dataset["observations"][i], act=dataset["actions"][i], rew=dataset["rewards"][i], done=dataset["terminals"][i], obs_next=dataset["next_observations"][i], ) ) print("dataset loaded") # trainer result = offline_trainer( policy, replay_buffer, test_collector, args.epoch, args.step_per_epoch, args.test_num, args.batch_size, save_best_fn=save_best_fn, logger=logger, ) pprint.pprint(result) else: watch() # Let's watch its performance! policy.eval() test_envs.seed(args.seed) test_collector.reset() result = test_collector.collect(n_episode=args.test_num, render=args.render) print(f"Final reward: {result['rews'].mean()}, length: {result['lens'].mean()}")