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_check_spaces_async_vector_env(shared_memory): # 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 = AsyncVectorEnv(env_fns, shared_memory=shared_memory) env.close(terminate=True)
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_async_vector_env(shared_memory): # 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 = AsyncVectorEnv(env_fns, shared_memory=shared_memory) env.close(terminate=True)
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_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_step_sync_vector_env(use_single_action_space): env_fns = [make_env("FrozenLake-v1", i) for i in range(8)] try: env = SyncVectorEnv(env_fns) observations = env.reset() assert isinstance(env.single_action_space, Discrete) assert isinstance(env.action_space, MultiDiscrete) 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, MultiDiscrete) 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_step_async_vector_env(shared_memory, use_single_action_space): env_fns = [make_env("CubeCrash-v0", i) for i in range(8)] try: env = AsyncVectorEnv(env_fns, shared_memory=shared_memory) 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_step_out_of_order_async_vector_env(shared_memory): env_fns = [make_env("CartPole-v1", i) for i in range(4)] with pytest.raises(NoAsyncCallError): try: env = AsyncVectorEnv(env_fns, shared_memory=shared_memory) actions = env.action_space.sample() observations = env.reset() observations, rewards, dones, infos = env.step_wait() except AlreadyPendingCallError as exception: assert exception.name == "step" raise finally: env.close(terminate=True) with pytest.raises(AlreadyPendingCallError): try: env = AsyncVectorEnv(env_fns, shared_memory=shared_memory) actions = env.action_space.sample() env.reset_async() env.step_async(actions) except AlreadyPendingCallError as exception: assert exception.name == "reset" raise finally: env.close(terminate=True)
def test_create_async_vector_env(shared_memory): env_fns = [make_env("CartPole-v1", i) for i in range(8)] try: env = AsyncVectorEnv(env_fns, shared_memory=shared_memory) finally: env.close() assert env.num_envs == 8
def test_no_copy_async_vector_env(shared_memory): env_fns = [make_env("CartPole-v1", i) for i in range(8)] try: env = AsyncVectorEnv(env_fns, shared_memory=shared_memory, copy=False) observations = env.reset() observations[0] = 0 finally: 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_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 test_no_copy_async_vector_env(shared_memory): env_fns = [make_env("CubeCrash-v0", i) for i in range(8)] try: env = AsyncVectorEnv(env_fns, shared_memory=shared_memory, copy=False) observations = env.reset() observations[0] = 128 assert np.all(env.observations[0] == 128) finally: 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])
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_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 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("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_already_closed_async_vector_env(shared_memory): env_fns = [make_env("CubeCrash-v0", i) for i in range(4)] with pytest.raises(ClosedEnvironmentError): env = AsyncVectorEnv(env_fns, shared_memory=shared_memory) env.close() observations = env.reset()
def test_already_closed_async_vector_env(shared_memory): env_fns = [make_env("CartPole-v1", i) for i in range(4)] with pytest.raises(ClosedEnvironmentError): env = AsyncVectorEnv(env_fns, shared_memory=shared_memory) env.close() env.reset()