def test_check_spaces_sync_vector_env(): # CartPole-v1 - observation_space: Box(4,), action_space: Discrete(2) env_fns = [make_env("CartPole-v1", i) for i in range(8)] # FrozenLake-v1 - Discrete(16), action_space: Discrete(4) env_fns[1] = make_env("FrozenLake-v1", 1) with pytest.raises(RuntimeError): env = SyncVectorEnv(env_fns) env.close()
def test_check_observations_sync_vector_env(): # CubeCrash-v0 - observation_space: Box(40, 32, 3) env_fns = [make_env('CubeCrash-v0', i) for i in range(8)] # MemorizeDigits-v0 - observation_space: Box(24, 32, 3) env_fns[1] = make_env('MemorizeDigits-v0', 1) with pytest.raises(RuntimeError): env = SyncVectorEnv(env_fns) env.close()
def test_create_sync_vector_env(): env_fns = [make_env("FrozenLake-v1", i) for i in range(8)] try: env = SyncVectorEnv(env_fns) finally: env.close() assert env.num_envs == 8
def test_create_sync_vector_env(): env_fns = [make_env('CubeCrash-v0', i) for i in range(8)] try: env = SyncVectorEnv(env_fns) finally: env.close() assert env.num_envs == 8
def test_sync_vector_determinism(spec: EnvSpec, seed: int = 123, n: int = 3): """Check that for all environments, the sync vector envs produce the same action samples using the same seeds""" env_1 = SyncVectorEnv([make_env(spec.id, seed=seed) for _ in range(n)]) env_2 = SyncVectorEnv([make_env(spec.id, seed=seed) for _ in range(n)]) assert_rng_equal(env_1.action_space.np_random, env_2.action_space.np_random) for _ in range(100): env_1_samples = env_1.action_space.sample() env_2_samples = env_2.action_space.sample() assert np.all(env_1_samples == env_2_samples)
def test_reset_sync_vector_env(): env_fns = [make_env('CubeCrash-v0', i) for i in range(8)] try: env = SyncVectorEnv(env_fns) observations = env.reset() finally: env.close() assert isinstance(env.observation_space, Box) assert isinstance(observations, np.ndarray) assert observations.dtype == env.observation_space.dtype assert observations.shape == (8, ) + env.single_observation_space.shape assert observations.shape == env.observation_space.shape
def test_vector_env_info_concurrent_termination(concurrent_ends): # envs that need to terminate together will have the same action actions = [0] * concurrent_ends + [1] * (NUM_ENVS - concurrent_ends) envs = [make_env(ENV_ID, SEED) for _ in range(NUM_ENVS)] envs = SyncVectorEnv(envs) for _ in range(ENV_STEPS): _, _, dones, infos = envs.step(actions) if any(dones): for i, done in enumerate(dones): if i < concurrent_ends: assert done assert infos["_terminal_observation"][i] else: assert not infos["_terminal_observation"][i] assert infos["terminal_observation"][i] is None return
def test_sync_vector_env_seed(): env = make_env("BipedalWalker-v3", seed=123)() sync_vector_env = SyncVectorEnv([make_env("BipedalWalker-v3", seed=123)]) assert_rng_equal(env.action_space.np_random, sync_vector_env.action_space.np_random) for _ in range(100): env_action = env.action_space.sample() vector_action = sync_vector_env.action_space.sample() assert np.all(env_action == vector_action)
def test_set_attr_sync_vector_env(): env_fns = [make_env("CartPole-v1", i) for i in range(4)] try: env = SyncVectorEnv(env_fns) env.set_attr("gravity", [9.81, 3.72, 8.87, 1.62]) gravity = env.get_attr("gravity") assert gravity == (9.81, 3.72, 8.87, 1.62) finally: env.close()
def make(id, num_envs=1, asynchronous=True, wrappers=None, **kwargs): """Create a vectorized environment from multiple copies of an environment, from its id Parameters ---------- id : str The environment ID. This must be a valid ID from the registry. num_envs : int Number of copies of the environment. asynchronous : bool (default: `True`) If `True`, wraps the environments in an `AsyncVectorEnv` (which uses `multiprocessing` to run the environments in parallel). If `False`, wraps the environments in a `SyncVectorEnv`. wrappers : Callable or Iterable of Callables (default: `None`) If not `None`, then apply the wrappers to each internal environment during creation. Returns ------- env : `gym.vector.VectorEnv` instance The vectorized environment. Example ------- >>> import gym >>> env = gym.vector.make('CartPole-v1', 3) >>> env.reset() array([[-0.04456399, 0.04653909, 0.01326909, -0.02099827], [ 0.03073904, 0.00145001, -0.03088818, -0.03131252], [ 0.03468829, 0.01500225, 0.01230312, 0.01825218]], dtype=float32) """ from gym.envs import make as make_ def _make_env(): env = make_(id, **kwargs) if wrappers is not None: if callable(wrappers): env = wrappers(env) elif isinstance(wrappers, Iterable) and all( [callable(w) for w in wrappers] ): for wrapper in wrappers: env = wrapper(env) else: raise NotImplementedError return env env_fns = [_make_env for _ in range(num_envs)] return AsyncVectorEnv(env_fns) if asynchronous else SyncVectorEnv(env_fns)
def make( id: str, num_envs: int = 1, asynchronous: bool = True, wrappers: Optional[Union[callable, List[callable]]] = None, disable_env_checker: bool = False, **kwargs, ) -> VectorEnv: """Create a vectorized environment from multiple copies of an environment, from its id. Example:: >>> import gym >>> env = gym.vector.make('CartPole-v1', num_envs=3) >>> env.reset() array([[-0.04456399, 0.04653909, 0.01326909, -0.02099827], [ 0.03073904, 0.00145001, -0.03088818, -0.03131252], [ 0.03468829, 0.01500225, 0.01230312, 0.01825218]], dtype=float32) Args: id: The environment ID. This must be a valid ID from the registry. num_envs: Number of copies of the environment. asynchronous: If `True`, wraps the environments in an :class:`AsyncVectorEnv` (which uses `multiprocessing`_ to run the environments in parallel). If ``False``, wraps the environments in a :class:`SyncVectorEnv`. wrappers: If not ``None``, then apply the wrappers to each internal environment during creation. disable_env_checker: If to disable the env checker, if True it will only run on the first environment created. **kwargs: Keywords arguments applied during gym.make Returns: The vectorized environment. """ def create_env(_disable_env_checker): """Creates an environment that can enable or disable the environment checker.""" def _make_env(): env = gym.envs.registration.make( id, disable_env_checker=_disable_env_checker, **kwargs) if wrappers is not None: if callable(wrappers): env = wrappers(env) elif isinstance(wrappers, Iterable) and all( [callable(w) for w in wrappers]): for wrapper in wrappers: env = wrapper(env) else: raise NotImplementedError return env return _make_env env_fns = [ create_env(disable_env_checker or env_num > 0) for env_num in range(num_envs) ] return AsyncVectorEnv(env_fns) if asynchronous else SyncVectorEnv(env_fns)
def test_step_sync_vector_env(use_single_action_space): env_fns = [make_env('CubeCrash-v0', i) for i in range(8)] try: env = SyncVectorEnv(env_fns) observations = env.reset() if use_single_action_space: actions = [env.single_action_space.sample() for _ in range(8)] else: actions = env.action_space.sample() observations, rewards, dones, _ = env.step(actions) finally: env.close() assert isinstance(env.observation_space, Box) assert isinstance(observations, np.ndarray) assert observations.dtype == env.observation_space.dtype assert observations.shape == (8, ) + env.single_observation_space.shape assert observations.shape == env.observation_space.shape assert isinstance(rewards, np.ndarray) assert isinstance(rewards[0], (float, np.floating)) assert rewards.ndim == 1 assert rewards.size == 8 assert isinstance(dones, np.ndarray) assert dones.dtype == np.bool_ assert dones.ndim == 1 assert dones.size == 8
def test_custom_space_sync_vector_env(): env_fns = [make_custom_space_env(i) for i in range(4)] try: env = SyncVectorEnv(env_fns) reset_observations = env.reset() assert isinstance(env.single_action_space, CustomSpace) assert isinstance(env.action_space, Tuple) actions = ("action-2", "action-3", "action-5", "action-7") step_observations, rewards, dones, _ = env.step(actions) finally: env.close() assert isinstance(env.single_observation_space, CustomSpace) assert isinstance(env.observation_space, Tuple) assert isinstance(reset_observations, tuple) assert reset_observations == ("reset", "reset", "reset", "reset") assert isinstance(step_observations, tuple) assert step_observations == ( "step(action-2)", "step(action-3)", "step(action-5)", "step(action-7)", )
def test_vector_env_equal(shared_memory): env_fns = [make_env("CubeCrash-v0", i) for i in range(4)] num_steps = 100 try: async_env = AsyncVectorEnv(env_fns, shared_memory=shared_memory) sync_env = SyncVectorEnv(env_fns) async_env.seed(0) sync_env.seed(0) assert async_env.num_envs == sync_env.num_envs assert async_env.observation_space == sync_env.observation_space assert async_env.single_observation_space == sync_env.single_observation_space assert async_env.action_space == sync_env.action_space assert async_env.single_action_space == sync_env.single_action_space async_observations = async_env.reset() sync_observations = sync_env.reset() assert np.all(async_observations == sync_observations) for _ in range(num_steps): actions = async_env.action_space.sample() assert actions in sync_env.action_space # fmt: off async_observations, async_rewards, async_dones, async_infos = async_env.step( actions) sync_observations, sync_rewards, sync_dones, sync_infos = sync_env.step( actions) # fmt: on for idx in range(len(sync_dones)): if sync_dones[idx]: assert "terminal_observation" in async_infos[idx] assert "terminal_observation" in sync_infos[idx] assert sync_dones[idx] assert np.all(async_observations == sync_observations) assert np.all(async_rewards == sync_rewards) assert np.all(async_dones == sync_dones) finally: async_env.close() sync_env.close()
def main(): np.set_printoptions(suppress=True, formatter={'float_kind': '{:0.2f}'.format}) env_fns = [make_env('MountainCar-v0', i) for i in range(4)] try: env = SyncVectorEnv(env_fns) finally: env.close() state_size = env.observation_space.shape[1] action_size = env.action_space[0].n NUM_EPISODES = 1000 STEPS_PER_EPISODE = 200 batch_size = 32 eps_mean_reward = [0.0] * NUM_EPISODES agent = DQNAgent(state_size, action_size) start_time = datetime.now() for ep_count in range(NUM_EPISODES): episode_rew = 0 state = env.reset() if (ep_count == 0): print("ep={} state.shape={}".format(ep_count, state.shape)) #state = np.reshape(state, [-1, state_size]) ep_start_time = datetime.now() for time in range(STEPS_PER_EPISODE): # env.render() action = agent.act(state) next_state, reward, done, _ = env.step(action) episode_rew += np.sum(reward) #next_state = np.reshape(next_state, [-1, state_size]) if (time == 0): print("ep={} time={} action.len={} next_state.shape={} elaps_time={}".format( \ ep_count, time, len(action), next_state.shape, (datetime.now() - ep_start_time)) ) #add to DQN buffer for idx in range(0, env.num_envs): agent.memorize(state[idx], action[idx], reward[idx], next_state[idx], done[idx]) state = next_state if time >= STEPS_PER_EPISODE - 1: eps_mean_reward[ep_count] = np.mean(episode_rew) / time print("ep: {}/{}, mean_avg_reward: {}, exec_time= {}".format( \ ep_count , NUM_EPISODES, eps_mean_reward[ep_count], (datetime.now() - ep_start_time))) #update DQN model if there are enough samples if len(agent.memory) > batch_size and time % 8 == 0: agent.replay(batch_size) #if ep_count % 2 == 0: # agent.save(str(os.path.join(save_path,'ma-foraging-dqn.h5'))) print("Finish train DQN Agent with {} episodes in {}".format( NUM_EPISODES, (datetime.now() - start_time)))
def make_env( env_id: str, num_envs: int = 1, ): def _make(): _env = gym.make(env_id) return _env if num_envs == 1: env = SyncVectorEnv([_make]) if num_envs > 1: env = AsyncVectorEnv([_make for _ in range(num_envs)]) dummy_env = _make() setattr(env, "spec", dummy_env.spec) del dummy_env else: env = _make() return env
def make(id, num_envs=1, asynchronous=True, **kwargs): """Create a vectorized environment from multiple copies of an environment, from its id Parameters ---------- id : str The environment ID. This must be a valid ID from the registry. num_envs : int Number of copies of the environment. If `1`, then it returns an unwrapped (i.e. non-vectorized) environment. asynchronous : bool (default: `True`) If `True`, wraps the environments in an `AsyncVectorEnv` (which uses `multiprocessing` to run the environments in parallel). If `False`, wraps the environments in a `SyncVectorEnv`. Returns ------- env : `gym.vector.VectorEnv` instance The vectorized environment. Example ------- >>> import gym >>> env = gym.vector.make('CartPole-v1', 3) >>> env.reset() array([[-0.04456399, 0.04653909, 0.01326909, -0.02099827], [ 0.03073904, 0.00145001, -0.03088818, -0.03131252], [ 0.03468829, 0.01500225, 0.01230312, 0.01825218]], dtype=float32) """ from gym.envs import make as make_ def _make_env(): return make_(id, **kwargs) if num_envs == 1: return _make_env() env_fns = [_make_env for _ in range(num_envs)] return AsyncVectorEnv(env_fns) if asynchronous else SyncVectorEnv(env_fns)
def test_call_sync_vector_env(): env_fns = [make_env("CartPole-v1", i) for i in range(4)] try: env = SyncVectorEnv(env_fns) _ = env.reset() images = env.call("render", mode="rgb_array") gravity = env.call("gravity") finally: env.close() assert isinstance(images, tuple) assert len(images) == 4 for i in range(4): assert isinstance(images[i], np.ndarray) assert isinstance(gravity, tuple) assert len(gravity) == 4 for i in range(4): assert isinstance(gravity[i], float) assert gravity[i] == 9.8
def test_vector_env_equal(shared_memory): env_fns = [make_env('CubeCrash-v0', i) for i in range(4)] num_steps = 100 try: async_env = AsyncVectorEnv(env_fns, shared_memory=shared_memory) sync_env = SyncVectorEnv(env_fns) async_env.seed(0) sync_env.seed(0) assert async_env.num_envs == sync_env.num_envs assert async_env.observation_space == sync_env.observation_space assert async_env.single_observation_space == sync_env.single_observation_space assert async_env.action_space == sync_env.action_space assert async_env.single_action_space == sync_env.single_action_space async_observations = async_env.reset() sync_observations = sync_env.reset() assert np.all(async_observations == sync_observations) for _ in range(num_steps): actions = async_env.action_space.sample() assert actions in sync_env.action_space async_observations, async_rewards, async_dones, _ = async_env.step( actions) sync_observations, sync_rewards, sync_dones, _ = sync_env.step( actions) assert np.all(async_observations == sync_observations) assert np.all(async_rewards == sync_rewards) assert np.all(async_dones == sync_dones) finally: async_env.close() sync_env.close()
def test_custom_space_sync_vector_env(): env_fns = [make_custom_space_env(i) for i in range(4)] try: env = SyncVectorEnv(env_fns) reset_observations = env.reset() actions = ('action-2', 'action-3', 'action-5', 'action-7') step_observations, rewards, dones, _ = env.step(actions) finally: env.close() assert isinstance(env.single_observation_space, CustomSpace) assert isinstance(env.observation_space, Tuple) assert isinstance(reset_observations, tuple) assert reset_observations == ('reset', 'reset', 'reset', 'reset') assert isinstance(step_observations, tuple) assert step_observations == ('step(action-2)', 'step(action-3)', 'step(action-5)', 'step(action-7)')
def test_vector_env_equal(shared_memory): env_fns = [make_env("CartPole-v1", i) for i in range(4)] num_steps = 100 try: async_env = AsyncVectorEnv(env_fns, shared_memory=shared_memory) sync_env = SyncVectorEnv(env_fns) assert async_env.num_envs == sync_env.num_envs assert async_env.observation_space == sync_env.observation_space assert async_env.single_observation_space == sync_env.single_observation_space assert async_env.action_space == sync_env.action_space assert async_env.single_action_space == sync_env.single_action_space async_observations = async_env.reset(seed=0) sync_observations = sync_env.reset(seed=0) assert np.all(async_observations == sync_observations) for _ in range(num_steps): actions = async_env.action_space.sample() assert actions in sync_env.action_space # fmt: off async_observations, async_rewards, async_dones, async_infos = async_env.step( actions) sync_observations, sync_rewards, sync_dones, sync_infos = sync_env.step( actions) # fmt: on if any(sync_dones): assert "terminal_observation" in async_infos assert "_terminal_observation" in async_infos assert "terminal_observation" in sync_infos assert "_terminal_observation" in sync_infos assert np.all(async_observations == sync_observations) assert np.all(async_rewards == sync_rewards) assert np.all(async_dones == sync_dones) finally: async_env.close() sync_env.close()
def test_reset_sync_vector_env(): env_fns = [make_env("CartPole-v1", i) for i in range(8)] try: env = SyncVectorEnv(env_fns) observations = env.reset() finally: env.close() assert isinstance(env.observation_space, Box) assert isinstance(observations, np.ndarray) assert observations.dtype == env.observation_space.dtype assert observations.shape == (8,) + env.single_observation_space.shape assert observations.shape == env.observation_space.shape del observations try: env = SyncVectorEnv(env_fns) observations = env.reset(return_info=False) finally: env.close() assert isinstance(env.observation_space, Box) assert isinstance(observations, np.ndarray) assert observations.dtype == env.observation_space.dtype assert observations.shape == (8,) + env.single_observation_space.shape assert observations.shape == env.observation_space.shape del observations env_fns = [make_env("CartPole-v1", i) for i in range(8)] try: env = SyncVectorEnv(env_fns) observations, infos = env.reset(return_info=True) finally: env.close() assert isinstance(env.observation_space, Box) assert isinstance(observations, np.ndarray) assert observations.dtype == env.observation_space.dtype assert observations.shape == (8,) + env.single_observation_space.shape assert observations.shape == env.observation_space.shape assert isinstance(infos, list) assert all([isinstance(info, dict) for info in infos])