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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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6
tests/envs/test_determinism.py
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@@ -10,16 +10,14 @@ def test_env(spec):
# threads. However, we probably already can't do multithreading
# due to some environments.
env1 = spec.make()
env1.seed(0)
initial_observation1 = env1.reset()
initial_observation1 = env1.reset(seed=0)
env1.action_space.seed(0)
action_samples1 = [env1.action_space.sample() for i in range(4)]
step_responses1 = [env1.step(action) for action in action_samples1]
env1.close()
env2 = spec.make()
env2.seed(0)
initial_observation2 = env2.reset()
initial_observation2 = env2.reset(seed=0)
env2.action_space.seed(0)
action_samples2 = [env2.action_space.sample() for i in range(4)]
step_responses2 = [env2.step(action) for action in action_samples2]