mirror of
https://github.com/Farama-Foundation/Gymnasium.git
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9acf9cd367
* add pygame GUI for frozen_lake.py env * add new line at EOF * pre-commit reformat * improve graphics * new images and dynamic window size * darker tile borders and fix ICC profile * pre-commit hook * adjust elf and stool size * Update frozen_lake.py * reformat * fix #2600 * #2600 * add rgb_array support * reformat * test render api change on FrozenLake * add render support for reset on frozenlake * add clock on pygame render * new render api for blackjack * new render api for cliffwalking * new render api for Env class * update reset method, lunar and Env * fix wrapper * fix reset lunar * new render api for box2d envs * new render api for mujoco envs * fix bug * new render api for classic control envs * fix tests * add render_mode None for CartPole * new render api for test fake envs * pre-commit hook * fix FrozenLake * fix FrozenLake * more render_mode to super - frozenlake * remove kwargs from frozen_lake new * pre-commit hook * add deprecated render method * add backwards compatibility * fix test * add _render * move pygame.init() (avoid pygame dependency on init) * fix pygame dependencies * remove collect_render() maintain multi-behaviours .render() * add type hints * fix renderer * don't call .render() with None * improve docstring * add single_rgb_array to all envs * remove None from metadata["render_modes"] * add type hints to test_env_checkers * fix lint * add comments to renderer * add comments to single_depth_array and single_state_pixels * reformat * add deprecation warnings and env.render_mode declaration * fix lint * reformat * fix tests * add docs * fix car racing determinism * remove warning test envs, customizable modes on renderer * remove commments and add todo for env_checker * fix car racing * replace render mode check with assert * update new mujoco * reformat * reformat * change metaclass definition * fix tests * implement mark suggestions (test, docs, sets) * check_render Co-authored-by: J K Terry <jkterry0@gmail.com>
202 lines
6.6 KiB
Python
202 lines
6.6 KiB
Python
import numpy as np
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import pytest
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from gym.envs.registration import EnvSpec
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from gym.spaces import Box, Discrete, MultiDiscrete, Tuple
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from gym.vector.sync_vector_env import SyncVectorEnv
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from tests.envs.spec_list import spec_list
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from tests.vector.utils import (
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CustomSpace,
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assert_rng_equal,
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make_custom_space_env,
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make_env,
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)
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def test_create_sync_vector_env():
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env_fns = [make_env("FrozenLake-v1", i) for i in range(8)]
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try:
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env = SyncVectorEnv(env_fns)
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finally:
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env.close()
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assert env.num_envs == 8
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def test_reset_sync_vector_env():
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env_fns = [make_env("CartPole-v1", i) for i in range(8)]
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try:
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env = SyncVectorEnv(env_fns)
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observations = env.reset()
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finally:
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env.close()
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assert isinstance(env.observation_space, Box)
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assert isinstance(observations, np.ndarray)
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assert observations.dtype == env.observation_space.dtype
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assert observations.shape == (8,) + env.single_observation_space.shape
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assert observations.shape == env.observation_space.shape
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del observations
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try:
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env = SyncVectorEnv(env_fns)
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observations = env.reset(return_info=False)
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finally:
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env.close()
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assert isinstance(env.observation_space, Box)
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assert isinstance(observations, np.ndarray)
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assert observations.dtype == env.observation_space.dtype
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assert observations.shape == (8,) + env.single_observation_space.shape
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assert observations.shape == env.observation_space.shape
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del observations
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env_fns = [make_env("CartPole-v1", i) for i in range(8)]
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try:
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env = SyncVectorEnv(env_fns)
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observations, infos = env.reset(return_info=True)
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finally:
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env.close()
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assert isinstance(env.observation_space, Box)
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assert isinstance(observations, np.ndarray)
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assert observations.dtype == env.observation_space.dtype
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assert observations.shape == (8,) + env.single_observation_space.shape
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assert observations.shape == env.observation_space.shape
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assert isinstance(infos, dict)
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assert all([isinstance(info, dict) for info in infos])
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@pytest.mark.parametrize("use_single_action_space", [True, False])
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def test_step_sync_vector_env(use_single_action_space):
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env_fns = [make_env("FrozenLake-v1", i) for i in range(8)]
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try:
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env = SyncVectorEnv(env_fns)
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observations = env.reset()
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assert isinstance(env.single_action_space, Discrete)
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assert isinstance(env.action_space, MultiDiscrete)
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if use_single_action_space:
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actions = [env.single_action_space.sample() for _ in range(8)]
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else:
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actions = env.action_space.sample()
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observations, rewards, dones, _ = env.step(actions)
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finally:
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env.close()
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assert isinstance(env.observation_space, MultiDiscrete)
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assert isinstance(observations, np.ndarray)
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assert observations.dtype == env.observation_space.dtype
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assert observations.shape == (8,) + env.single_observation_space.shape
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assert observations.shape == env.observation_space.shape
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assert isinstance(rewards, np.ndarray)
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assert isinstance(rewards[0], (float, np.floating))
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assert rewards.ndim == 1
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assert rewards.size == 8
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assert isinstance(dones, np.ndarray)
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assert dones.dtype == np.bool_
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assert dones.ndim == 1
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assert dones.size == 8
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def test_call_sync_vector_env():
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env_fns = [make_env("CartPole-v1", i, render_mode="rgb_array") for i in range(4)]
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try:
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env = SyncVectorEnv(env_fns)
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_ = env.reset()
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images = env.call("render")
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gravity = env.call("gravity")
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finally:
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env.close()
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assert isinstance(images, tuple)
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assert len(images) == 4
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for i in range(4):
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assert len(images[i]) == 1
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assert isinstance(images[i][0], np.ndarray)
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assert isinstance(gravity, tuple)
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assert len(gravity) == 4
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for i in range(4):
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assert isinstance(gravity[i], float)
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assert gravity[i] == 9.8
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def test_set_attr_sync_vector_env():
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env_fns = [make_env("CartPole-v1", i) for i in range(4)]
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try:
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env = SyncVectorEnv(env_fns)
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env.set_attr("gravity", [9.81, 3.72, 8.87, 1.62])
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gravity = env.get_attr("gravity")
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assert gravity == (9.81, 3.72, 8.87, 1.62)
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finally:
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env.close()
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def test_check_spaces_sync_vector_env():
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# CartPole-v1 - observation_space: Box(4,), action_space: Discrete(2)
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env_fns = [make_env("CartPole-v1", i) for i in range(8)]
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# FrozenLake-v1 - Discrete(16), action_space: Discrete(4)
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env_fns[1] = make_env("FrozenLake-v1", 1)
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with pytest.raises(RuntimeError):
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env = SyncVectorEnv(env_fns)
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env.close()
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def test_custom_space_sync_vector_env():
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env_fns = [make_custom_space_env(i) for i in range(4)]
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try:
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env = SyncVectorEnv(env_fns)
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reset_observations = env.reset()
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assert isinstance(env.single_action_space, CustomSpace)
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assert isinstance(env.action_space, Tuple)
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actions = ("action-2", "action-3", "action-5", "action-7")
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step_observations, rewards, dones, _ = env.step(actions)
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finally:
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env.close()
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assert isinstance(env.single_observation_space, CustomSpace)
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assert isinstance(env.observation_space, Tuple)
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assert isinstance(reset_observations, tuple)
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assert reset_observations == ("reset", "reset", "reset", "reset")
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assert isinstance(step_observations, tuple)
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assert step_observations == (
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"step(action-2)",
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"step(action-3)",
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"step(action-5)",
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"step(action-7)",
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)
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def test_sync_vector_env_seed():
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env = make_env("BipedalWalker-v3", seed=123)()
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sync_vector_env = SyncVectorEnv([make_env("BipedalWalker-v3", seed=123)])
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assert_rng_equal(env.action_space.np_random, sync_vector_env.action_space.np_random)
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for _ in range(100):
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env_action = env.action_space.sample()
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vector_action = sync_vector_env.action_space.sample()
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assert np.all(env_action == vector_action)
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@pytest.mark.parametrize("spec", spec_list, ids=[spec.id for spec in spec_list])
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def test_sync_vector_determinism(spec: EnvSpec, seed: int = 123, n: int = 3):
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"""Check that for all environments, the sync vector envs produce the same action samples using the same seeds"""
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env_1 = SyncVectorEnv([make_env(spec.id, seed=seed) for _ in range(n)])
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env_2 = SyncVectorEnv([make_env(spec.id, seed=seed) for _ in range(n)])
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assert_rng_equal(env_1.action_space.np_random, env_2.action_space.np_random)
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for _ in range(100):
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env_1_samples = env_1.action_space.sample()
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env_2_samples = env_2.action_space.sample()
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assert np.all(env_1_samples == env_2_samples)
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