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Gymnasium/tests/experimental/wrappers/test_lambda_action.py
2022-12-05 19:14:56 +00:00

79 lines
2.5 KiB
Python

"""Test suit for lambda action wrappers: LambdaAction, ClipAction, RescaleAction."""
import numpy as np
from gymnasium.experimental.wrappers import (
ClipActionV0,
LambdaActionV0,
RescaleActionV0,
)
from gymnasium.spaces import Box
from tests.testing_env import GenericTestEnv
SEED = 42
def _record_action_step_func(self, action):
return 0, 0, False, False, {"action": action}
def test_lambda_action_wrapper():
"""Tests LambdaAction through checking that the action taken is transformed by function."""
env = GenericTestEnv(step_func=_record_action_step_func)
wrapped_env = LambdaActionV0(env, lambda action: action - 2, Box(2, 3))
sampled_action = wrapped_env.action_space.sample()
assert sampled_action not in env.action_space
_, _, _, _, info = wrapped_env.step(sampled_action)
assert info["action"] in env.action_space
assert sampled_action - 2 == info["action"]
def test_clip_action_wrapper():
"""Test that the action is correctly clipped to the base environment action space."""
env = GenericTestEnv(
action_space=Box(np.array([0, 0, 3]), np.array([1, 2, 4])),
step_func=_record_action_step_func,
)
wrapped_env = ClipActionV0(env)
sampled_action = np.array([-1, 5, 3.5], dtype=np.float32)
assert sampled_action not in env.action_space
assert sampled_action in wrapped_env.action_space
_, _, _, _, info = wrapped_env.step(sampled_action)
assert np.all(info["action"] in env.action_space)
assert np.all(info["action"] == np.array([0, 2, 3.5]))
def test_rescale_action_wrapper():
"""Test that the action is rescale within a min / max bound."""
env = GenericTestEnv(
step_func=_record_action_step_func,
action_space=Box(np.array([0, 1]), np.array([1, 3])),
)
wrapped_env = RescaleActionV0(
env, min_action=np.array([-5, 0]), max_action=np.array([5, 1])
)
assert wrapped_env.action_space == Box(np.array([-5, 0]), np.array([5, 1]))
for sample_action, expected_action in (
(
np.array([0.0, 0.5], dtype=np.float32),
np.array([0.5, 2.0], dtype=np.float32),
),
(
np.array([-5.0, 0.0], dtype=np.float32),
np.array([0.0, 1.0], dtype=np.float32),
),
(
np.array([5.0, 1.0], dtype=np.float32),
np.array([1.0, 3.0], dtype=np.float32),
),
):
assert sample_action in wrapped_env.action_space
_, _, _, _, info = wrapped_env.step(sample_action)
assert np.all(info["action"] == expected_action)