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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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24
gym/envs/unittest/memorize_digits.py
View File

@@ -1,3 +1,5 @@
from typing import Optional
import numpy as np
import gym
from gym import spaces
@@ -60,7 +62,6 @@ class MemorizeDigits(gym.Env):
use_random_colors = False
def __init__(self):
self.seed()
self.viewer = None
self.observation_space = spaces.Box(
0, 255, (FIELD_H, FIELD_W, 3), dtype=np.uint8
@@ -74,22 +75,19 @@ class MemorizeDigits(gym.Env):
]
self.reset()
def seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def random_color(self):
return np.array(
[
self.np_random.randint(low=0, high=255),
self.np_random.randint(low=0, high=255),
self.np_random.randint(low=0, high=255),
self.np_random.integers(low=0, high=255),
self.np_random.integers(low=0, high=255),
self.np_random.integers(low=0, high=255),
]
).astype("uint8")
def reset(self):
self.digit_x = self.np_random.randint(low=FIELD_W // 5, high=FIELD_W // 5 * 4)
self.digit_y = self.np_random.randint(low=FIELD_H // 5, high=FIELD_H // 5 * 4)
def reset(self, seed: Optional[int] = None):
super().reset(seed=seed)
self.digit_x = self.np_random.integers(low=FIELD_W // 5, high=FIELD_W // 5 * 4)
self.digit_y = self.np_random.integers(low=FIELD_H // 5, high=FIELD_H // 5 * 4)
self.color_bg = self.random_color() if self.use_random_colors else color_black
self.step_n = 0
while 1:
@@ -111,8 +109,8 @@ class MemorizeDigits(gym.Env):
else:
if self.digit == action:
reward = +1
done = self.step_n > 20 and 0 == self.np_random.randint(low=0, high=5)
self.digit = self.np_random.randint(low=0, high=10)
done = self.step_n > 20 and 0 == self.np_random.integers(low=0, high=5)
self.digit = self.np_random.integers(low=0, high=10)
obs = np.zeros((FIELD_H, FIELD_W, 3), dtype=np.uint8)
obs[:, :, :] = self.color_bg
digit_img = np.zeros((6, 6, 3), dtype=np.uint8)