mirror of
https://github.com/Farama-Foundation/Gymnasium.git
synced 2025-08-22 07:02:19 +00:00
c364506710
* 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
27 lines
676 B
Python
27 lines
676 B
Python
import numpy as np
|
|
|
|
import gym
|
|
from gym.wrappers import ClipAction
|
|
|
|
|
|
def test_clip_action():
|
|
# mountaincar: action-based rewards
|
|
make_env = lambda: gym.make("MountainCarContinuous-v0")
|
|
env = make_env()
|
|
wrapped_env = ClipAction(make_env())
|
|
|
|
seed = 0
|
|
|
|
env.reset(seed=seed)
|
|
wrapped_env.reset(seed=seed)
|
|
|
|
actions = [[0.4], [1.2], [-0.3], [0.0], [-2.5]]
|
|
for action in actions:
|
|
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
|