def test_pg(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).to(args.device) optim = torch.optim.Adam(net.parameters(), lr=args.lr) dist = torch.distributions.Categorical policy = PGPolicy(net, optim, dist, args.gamma, reward_normalization=args.rew_norm, action_space=env.action_space) # collector train_collector = Collector( policy, train_envs, VectorReplayBuffer(args.buffer_size, len(train_envs)), exploration_noise=True) test_collector = Collector(policy, test_envs) # log log_path = os.path.join(args.logdir, args.task, 'pg') writer = SummaryWriter(log_path) logger = BasicLogger(writer) 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 # trainer result = onpolicy_trainer( policy, train_collector, test_collector, args.epoch, args.step_per_epoch, args.repeat_per_collect, args.test_num, args.batch_size, episode_per_collect=args.episode_per_collect, stop_fn=stop_fn, save_fn=save_fn, logger=logger) assert stop_fn(result['best_reward']) if __name__ == '__main__': pprint.pprint(result) # Let's watch its performance! env = gym.make(args.task) policy.eval() 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_reinforce(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] 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)) # train_envs = gym.make(args.task) train_envs = SubprocVectorEnv( [lambda: gym.make(args.task) for _ in range(args.training_num)], norm_obs=True) # test_envs = gym.make(args.task) test_envs = SubprocVectorEnv( [lambda: gym.make(args.task) for _ in range(args.test_num)], norm_obs=True, obs_rms=train_envs.obs_rms, update_obs_rms=False) # seed np.random.seed(args.seed) torch.manual_seed(args.seed) train_envs.seed(args.seed) test_envs.seed(args.seed) # model net_a = Net(args.state_shape, hidden_sizes=args.hidden_sizes, activation=nn.Tanh, device=args.device) actor = ActorProb(net_a, args.action_shape, max_action=args.max_action, unbounded=True, device=args.device).to(args.device) torch.nn.init.constant_(actor.sigma_param, -0.5) for m in actor.modules(): if isinstance(m, torch.nn.Linear): # orthogonal initialization torch.nn.init.orthogonal_(m.weight, gain=np.sqrt(2)) torch.nn.init.zeros_(m.bias) # do last policy layer scaling, this will make initial actions have (close to) # 0 mean and std, and will help boost performances, # see https://arxiv.org/abs/2006.05990, Fig.24 for details for m in actor.mu.modules(): if isinstance(m, torch.nn.Linear): torch.nn.init.zeros_(m.bias) m.weight.data.copy_(0.01 * m.weight.data) optim = torch.optim.Adam(actor.parameters(), lr=args.lr) 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) def dist(*logits): return Independent(Normal(*logits), 1) policy = PGPolicy(actor, optim, dist, discount_factor=args.gamma, reward_normalization=args.rew_norm, action_scaling=True, action_bound_method=args.action_bound_method, lr_scheduler=lr_scheduler, 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) # collector if args.training_num > 1: buffer = VectorReplayBuffer(args.buffer_size, len(train_envs)) else: buffer = ReplayBuffer(args.buffer_size) train_collector = Collector(policy, train_envs, buffer, exploration_noise=True) test_collector = Collector(policy, test_envs) # log t0 = datetime.datetime.now().strftime("%m%d_%H%M%S") log_file = f'seed_{args.seed}_{t0}-{args.task.replace("-", "_")}_reinforce' log_path = os.path.join(args.logdir, args.task, 'reinforce', log_file) writer = SummaryWriter(log_path) writer.add_text("args", str(args)) logger = BasicLogger(writer, update_interval=10, train_interval=100) def save_fn(policy): torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth')) if not args.watch: # trainer result = onpolicy_trainer(policy, train_collector, test_collector, args.epoch, args.step_per_epoch, args.repeat_per_collect, args.test_num, args.batch_size, step_per_collect=args.step_per_collect, save_fn=save_fn, logger=logger, test_in_train=False) pprint.pprint(result) # 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()}' )
def test_reinforce(args=get_args()): env, train_envs, test_envs = make_mujoco_env(args.task, args.seed, args.training_num, args.test_num, obs_norm=True) 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] 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)) # seed np.random.seed(args.seed) torch.manual_seed(args.seed) # model net_a = Net( args.state_shape, hidden_sizes=args.hidden_sizes, activation=nn.Tanh, device=args.device, ) actor = ActorProb( net_a, args.action_shape, max_action=args.max_action, unbounded=True, device=args.device, ).to(args.device) torch.nn.init.constant_(actor.sigma_param, -0.5) for m in actor.modules(): if isinstance(m, torch.nn.Linear): # orthogonal initialization torch.nn.init.orthogonal_(m.weight, gain=np.sqrt(2)) torch.nn.init.zeros_(m.bias) # do last policy layer scaling, this will make initial actions have (close to) # 0 mean and std, and will help boost performances, # see https://arxiv.org/abs/2006.05990, Fig.24 for details for m in actor.mu.modules(): if isinstance(m, torch.nn.Linear): torch.nn.init.zeros_(m.bias) m.weight.data.copy_(0.01 * m.weight.data) optim = torch.optim.Adam(actor.parameters(), lr=args.lr) 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) def dist(*logits): return Independent(Normal(*logits), 1) policy = PGPolicy( actor, optim, dist, discount_factor=args.gamma, reward_normalization=args.rew_norm, action_scaling=True, action_bound_method=args.action_bound_method, lr_scheduler=lr_scheduler, action_space=env.action_space, ) # load a previous policy if args.resume_path: ckpt = torch.load(args.resume_path, map_location=args.device) policy.load_state_dict(ckpt["model"]) train_envs.set_obs_rms(ckpt["obs_rms"]) test_envs.set_obs_rms(ckpt["obs_rms"]) print("Loaded agent from: ", args.resume_path) # collector if args.training_num > 1: buffer = VectorReplayBuffer(args.buffer_size, len(train_envs)) else: buffer = ReplayBuffer(args.buffer_size) train_collector = Collector(policy, train_envs, buffer, exploration_noise=True) test_collector = Collector(policy, test_envs) # log now = datetime.datetime.now().strftime("%y%m%d-%H%M%S") args.algo_name = "reinforce" 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): state = { "model": policy.state_dict(), "obs_rms": train_envs.get_obs_rms() } torch.save(state, os.path.join(log_path, "policy.pth")) if not args.watch: # trainer result = onpolicy_trainer( policy, train_collector, test_collector, args.epoch, args.step_per_epoch, args.repeat_per_collect, args.test_num, args.batch_size, step_per_collect=args.step_per_collect, save_best_fn=save_best_fn, logger=logger, test_in_train=False, ) pprint.pprint(result) # 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()}' )