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Seeding update (#2422)

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* 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
This commit is contained in:
Ariel Kwiatkowski
2021-12-08 22:14:15 +01:00
committed by GitHub
parent b84b69c872
commit c364506710
59 changed files with 386 additions and 294 deletions
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18
tests/wrappers/test_transform_reward.py
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@@ -15,10 +15,8 @@ def test_transform_reward(env_id):
wrapped_env = TransformReward(gym.make(env_id), lambda r: scale * r)
action = env.action_space.sample()
env.seed(0)
env.reset()
wrapped_env.seed(0)
wrapped_env.reset()
env.reset(seed=0)
wrapped_env.reset(seed=0)
_, reward, _, _ = env.step(action)
_, wrapped_reward, _, _ = wrapped_env.step(action)
@@ -33,10 +31,8 @@ def test_transform_reward(env_id):
wrapped_env = TransformReward(gym.make(env_id), lambda r: np.clip(r, min_r, max_r))
action = env.action_space.sample()
env.seed(0)
env.reset()
wrapped_env.seed(0)
wrapped_env.reset()
env.reset(seed=0)
wrapped_env.reset(seed=0)
_, reward, _, _ = env.step(action)
_, wrapped_reward, _, _ = wrapped_env.step(action)
@@ -49,10 +45,8 @@ def test_transform_reward(env_id):
env = gym.make(env_id)
wrapped_env = TransformReward(gym.make(env_id), lambda r: np.sign(r))
env.seed(0)
env.reset()
wrapped_env.seed(0)
wrapped_env.reset()
env.reset(seed=0)
wrapped_env.reset(seed=0)
for _ in range(1000):
action = env.action_space.sample()