mirror of
https://github.com/Farama-Foundation/Gymnasium.git
synced 2025-08-27 16:57:10 +00:00
* feat: add `isort` to `pre-commit` * ci: skip `__init__.py` file for `isort` * ci: make `isort` mandatory in lint pipeline * docs: add a section on Git hooks * ci: check isort diff * fix: isort from master branch * docs: add pre-commit badge * ci: update black + bandit versions * feat: add PR template * refactor: PR template * ci: remove bandit * docs: add Black badge * ci: try to remove all `|| true` statements * ci: remove lint_python job - Remove `lint_python` CI job - Move `pyupgrade` job to `pre-commit` workflow * fix: avoid messing with typing * docs: add a note on running `pre-cpmmit` manually * ci: apply `pre-commit` to the whole codebase
57 lines
1.5 KiB
Python
57 lines
1.5 KiB
Python
import numpy as np
|
|
import pytest
|
|
|
|
import gym
|
|
from gym.wrappers import TransformReward
|
|
|
|
|
|
@pytest.mark.parametrize("env_id", ["CartPole-v1", "Pendulum-v1"])
|
|
def test_transform_reward(env_id):
|
|
# use case #1: scale
|
|
scales = [0.1, 200]
|
|
for scale in scales:
|
|
env = gym.make(env_id)
|
|
wrapped_env = TransformReward(gym.make(env_id), lambda r: scale * r)
|
|
action = env.action_space.sample()
|
|
|
|
env.reset(seed=0)
|
|
wrapped_env.reset(seed=0)
|
|
|
|
_, reward, _, _ = env.step(action)
|
|
_, wrapped_reward, _, _ = wrapped_env.step(action)
|
|
|
|
assert wrapped_reward == scale * reward
|
|
del env, wrapped_env
|
|
|
|
# use case #2: clip
|
|
min_r = -0.0005
|
|
max_r = 0.0002
|
|
env = gym.make(env_id)
|
|
wrapped_env = TransformReward(gym.make(env_id), lambda r: np.clip(r, min_r, max_r))
|
|
action = env.action_space.sample()
|
|
|
|
env.reset(seed=0)
|
|
wrapped_env.reset(seed=0)
|
|
|
|
_, reward, _, _ = env.step(action)
|
|
_, wrapped_reward, _, _ = wrapped_env.step(action)
|
|
|
|
assert abs(wrapped_reward) < abs(reward)
|
|
assert wrapped_reward == -0.0005 or wrapped_reward == 0.0002
|
|
del env, wrapped_env
|
|
|
|
# use case #3: sign
|
|
env = gym.make(env_id)
|
|
wrapped_env = TransformReward(gym.make(env_id), lambda r: np.sign(r))
|
|
|
|
env.reset(seed=0)
|
|
wrapped_env.reset(seed=0)
|
|
|
|
for _ in range(1000):
|
|
action = env.action_space.sample()
|
|
_, wrapped_reward, done, _ = wrapped_env.step(action)
|
|
assert wrapped_reward in [-1.0, 0.0, 1.0]
|
|
if done:
|
|
break
|
|
del env, wrapped_env
|