mirror of
https://github.com/Farama-Foundation/Gymnasium.git
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0263deb5ab
* Add support for python 3.6 * Add support for python 3.6 * Added check for python 3.6 to not install mujoco as no version exists * Fixed the install groups for python 3.6 * Re-added python 3.6 support for gym * black * Added support for dataclasses through dataclasses module in setup that backports the module * Fixed install requirements * Re-added dummy env spec with dataclasses * Changed type for compatability for python 3.6 * Added a python 3.6 warning * Fixed python 3.6 typing issue * Removed __future__ import annotation for python 3.6 support * Fixed python 3.6 typing
116 lines
3.5 KiB
Python
116 lines
3.5 KiB
Python
from typing import List
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import numpy as np
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import pytest
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import gym
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from gym import Env
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from gym.envs.registration import EnvSpec
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from gym.spaces.box import Box
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from gym.spaces.discrete import Discrete
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from tests.envs.spec_list import (
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SKIP_MUJOCO_V3_WARNING_MESSAGE,
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skip_mujoco_v3,
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spec_list,
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)
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ENVIRONMENT_IDS = ("HalfCheetah-v2",)
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def filters_envs_action_space_type(
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env_spec_list: List[EnvSpec], action_space: type
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) -> List[Env]:
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"""Make environments of specific action_space type.
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This function returns a filtered list of environment from the spec_list that matches the action_space type.
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Args:
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env_spec_list (list): list of registered environments' specification
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action_space (gym.spaces.Space): action_space type
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"""
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filtered_envs = []
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for spec in env_spec_list:
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env = gym.make(spec.id)
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if isinstance(env.action_space, action_space):
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filtered_envs.append(env)
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return filtered_envs
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@pytest.mark.skipif(skip_mujoco_v3, reason=SKIP_MUJOCO_V3_WARNING_MESSAGE)
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@pytest.mark.parametrize("environment_id", ENVIRONMENT_IDS)
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def test_serialize_deserialize(environment_id):
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env = gym.make(environment_id)
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env.reset()
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with pytest.raises(ValueError, match="Action dimension mismatch"):
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env.step([0.1])
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with pytest.raises(ValueError, match="Action dimension mismatch"):
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env.step(0.1)
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@pytest.mark.parametrize("env", filters_envs_action_space_type(spec_list, Discrete))
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def test_discrete_actions_out_of_bound(env):
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"""Test out of bound actions in Discrete action_space.
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In discrete action_space environments, `out-of-bound`
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actions are not allowed and should raise an exception.
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Args:
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env (gym.Env): the gym environment
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"""
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env.reset()
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action_space = env.action_space
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upper_bound = action_space.start + action_space.n - 1
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with pytest.raises(Exception):
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env.step(upper_bound + 1)
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@pytest.mark.parametrize(
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("env", "seed"),
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[(env, 42) for env in filters_envs_action_space_type(spec_list, Box)],
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)
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def test_box_actions_out_of_bound(env, seed):
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"""Test out of bound actions in Box action_space.
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Environments with Box actions spaces perform clipping inside `step`.
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The expected behaviour is that an action `out-of-bound` has the same effect
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of an action with value exactly at the upper (or lower) bound.
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Args:
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env (gym.Env): the gym environment
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seed (int): seed value for determinism
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"""
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OOB_VALUE = 100
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env.reset(seed=seed)
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oob_env = gym.make(env.spec.id)
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oob_env.reset(seed=seed)
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dtype = env.action_space.dtype
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upper_bounds = env.action_space.high
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lower_bounds = env.action_space.low
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for i, (is_upper_bound, is_lower_bound) in enumerate(
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zip(env.action_space.bounded_above, env.action_space.bounded_below)
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):
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if is_upper_bound:
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obs, _, _, _ = env.step(upper_bounds)
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oob_action = upper_bounds.copy()
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oob_action[i] += np.cast[dtype](OOB_VALUE)
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assert oob_action[i] > upper_bounds[i]
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oob_obs, _, _, _ = oob_env.step(oob_action)
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assert np.alltrue(obs == oob_obs)
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if is_lower_bound:
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obs, _, _, _ = env.step(lower_bounds)
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oob_action = lower_bounds.copy()
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oob_action[i] -= np.cast[dtype](OOB_VALUE)
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assert oob_action[i] < lower_bounds[i]
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oob_obs, _, _, _ = oob_env.step(oob_action)
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assert np.alltrue(obs == oob_obs)
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