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Gymnasium/tests/wrappers/test_clip_action.py

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import numpy as np
import gym
from gym.wrappers import ClipAction
def test_clip_action():
# mountaincar: action-based rewards
env = gym.make("MountainCarContinuous-v0")
wrapped_env = ClipAction(gym.make("MountainCarContinuous-v0"))
seed = 0
Seeding update (#2422) * Ditch most of the seeding.py and replace np_random with the numpy default_rng. Let's see if tests pass * Updated a bunch of RNG calls from the RandomState API to Generator API * black; didn't expect that, did ya? * Undo a typo * blaaack * More typo fixes * Fixed setting/getting state in multidiscrete spaces * Fix typo, fix a test to work with the new sampling * Correctly (?) pass the randomly generated seed if np_random is called with None as seed * Convert the Discrete sample to a python int (as opposed to np.int64) * Remove some redundant imports * First version of the compatibility layer for old-style RNG. Mainly to trigger tests. * Removed redundant f-strings * Style fixes, removing unused imports * Try to make tests pass by removing atari from the dockerfile * Try to make tests pass by removing atari from the setup * Try to make tests pass by removing atari from the setup * Try to make tests pass by removing atari from the setup * First attempt at deprecating `env.seed` and supporting `env.reset(seed=seed)` instead. Tests should hopefully pass but throw up a million warnings. * black; didn't expect that, didya? * Rename the reset parameter in VecEnvs back to `seed` * Updated tests to use the new seeding method * Removed a bunch of old `seed` calls. Fixed a bug in AsyncVectorEnv * Stop Discrete envs from doing part of the setup (and using the randomness) in init (as opposed to reset) * Add explicit seed to wrappers reset * Remove an accidental return * Re-add some legacy functions with a warning. * Use deprecation instead of regular warnings for the newly deprecated methods/functions
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env.reset(seed=seed)
wrapped_env.reset(seed=seed)
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actions = [[0.4], [1.2], [-0.3], [0.0], [-2.5]]
for action in actions:
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obs1, r1, d1, _ = env.step(
np.clip(action, env.action_space.low, env.action_space.high)
)
obs2, r2, d2, _ = wrapped_env.step(action)
assert np.allclose(r1, r2)
assert np.allclose(obs1, obs2)
assert d1 == d2