Files
Gymnasium/tests/envs/test_envs.py
2022-05-24 08:47:51 -04:00

93 lines
3.0 KiB
Python

import numpy as np
import pytest
from gym import envs
from gym.spaces import Box
from gym.utils.env_checker import check_env
from tests.envs.spec_list import spec_list, spec_list_no_mujoco_py
# This runs a smoketest on each official registered env. We may want
# to try also running environments which are not officially registered
# envs.
@pytest.mark.filterwarnings(
"ignore:.*We recommend you to use a symmetric and normalized Box action space.*"
)
@pytest.mark.parametrize(
"spec", spec_list_no_mujoco_py, ids=[spec.id for spec in spec_list_no_mujoco_py]
)
def test_env(spec):
# Capture warnings
with pytest.warns(None) as warnings:
env = spec.make()
# Test if env adheres to Gym API
check_env(env, warn=True, skip_render_check=True)
# Check that dtype is explicitly declared for gym.Box spaces
for warning_msg in warnings:
assert "autodetected dtype" not in str(warning_msg.message)
ob_space = env.observation_space
act_space = env.action_space
ob = env.reset()
assert ob_space.contains(ob), f"Reset observation: {ob!r} not in space"
if isinstance(ob_space, Box):
# Only checking dtypes for Box spaces to avoid iterating through tuple entries
assert (
ob.dtype == ob_space.dtype
), f"Reset observation dtype: {ob.dtype}, expected: {ob_space.dtype}"
a = act_space.sample()
observation, reward, done, _info = env.step(a)
assert ob_space.contains(
observation
), f"Step observation: {observation!r} not in space"
assert np.isscalar(reward), f"{reward} is not a scalar for {env}"
assert isinstance(done, bool), f"Expected {done} to be a boolean"
if isinstance(ob_space, Box):
assert (
observation.dtype == ob_space.dtype
), f"Step observation dtype: {ob.dtype}, expected: {ob_space.dtype}"
for mode in env.metadata.get("render_modes", []):
if not (mode == "human" and spec.entry_point.startswith("gym.envs.mujoco")):
env.render(mode=mode)
# Make sure we can render the environment after close.
for mode in env.metadata.get("render_modes", []):
if not (mode == "human" and spec.entry_point.startswith("gym.envs.mujoco")):
env.render(mode=mode)
env.close()
@pytest.mark.parametrize("spec", spec_list, ids=[spec.id for spec in spec_list])
def test_reset_info(spec):
with pytest.warns(None):
env = spec.make()
ob_space = env.observation_space
obs = env.reset()
assert ob_space.contains(obs)
obs = env.reset(return_info=False)
assert ob_space.contains(obs)
obs, info = env.reset(return_info=True)
assert ob_space.contains(obs)
assert isinstance(info, dict)
env.close()
def test_env_render_result_is_immutable():
environs = [
envs.make("Taxi-v3"),
envs.make("FrozenLake-v1"),
]
for env in environs:
env.reset()
output = env.render(mode="ansi")
assert isinstance(output, str)
env.close()