如何使用OpenAi-Gym和Scoop产生可重现的随机性？

Question

如何使用OpenAi-Gym和Scoop产生可重现的随机性？

我希望每次重复这个例子时都能得到完全相同的结果。如果可能，我希望这与使用随机性提供程序(例如随机和np.random)的现有库一起工作，这可能是一个问题，因为它们通常使用全局随机状态，并且不提供局部随机状态的接口

我的示例脚本如下所示：

import random
import numpy as np
from scoop import futures
import gym


def do(it):
    random.seed(it)
    np.random.seed(it)
    env.seed(it)
    env.action_space.seed(it)
    env.reset()
    observations = []
    for i in range(3):
        while True:
            action = env.action_space.sample()
            ob, reward, done, _ = env.step(action)
            observations.append(ob)
            if done:
                break
    return observations


env = gym.make("BipedalWalker-v3")
if __name__ == "__main__":
    maxit = 20
    results1 = futures.map(do, range(2, maxit))
    results2 = futures.map(do, range(2, maxit))
    for a,b in zip(results1, results2):
        if np.array_equiv(a, b):
            print("equal, yay")
        else:
            print("not equal :(")

预期输出:每行都有equal, yay

实际输出:多行上的not equal :(

完整输出：

/home/chef/.venv/neuro/bin/python -m scoop /home/chef/dev/projekte/NeuroEvolution-CTRNN_new/random_test.py
[2020-05-18 18:05:03,578] launcher  INFO    SCOOP 0.7 1.1 on linux using Python 3.8.2 (default, Apr 27 2020, 15:53:34) [GCC 9.3.0], API: 1013
[2020-05-18 18:05:03,578] launcher  INFO    Deploying 4 worker(s) over 1 host(s).
[2020-05-18 18:05:03,578] launcher  INFO    Worker distribution: 
[2020-05-18 18:05:03,578] launcher  INFO       127.0.0.1:   3 + origin
/home/chef/.venv/neuro/lib/python3.8/site-packages/gym/logger.py:30: UserWarning: WARN: Box bound precision lowered by casting to float32
  warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))
/home/chef/.venv/neuro/lib/python3.8/site-packages/gym/logger.py:30: UserWarning: WARN: Box bound precision lowered by casting to float32
  warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))
/home/chef/.venv/neuro/lib/python3.8/site-packages/gym/logger.py:30: UserWarning: WARN: Box bound precision lowered by casting to float32
  warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))
/home/chef/.venv/neuro/lib/python3.8/site-packages/gym/logger.py:30: UserWarning: WARN: Box bound precision lowered by casting to float32
  warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))
equal, yay
not equal :(
not equal :(
not equal :(
not equal :(
not equal :(
equal, yay
not equal :(
equal, yay
equal, yay
equal, yay
equal, yay
equal, yay
not equal :(
equal, yay
equal, yay
equal, yay
not equal :(
[2020-05-18 18:05:08,554] launcher  (127.0.0.1:37729) INFO    Root process is done.
[2020-05-18 18:05:08,554] launcher  (127.0.0.1:37729) INFO    Finished cleaning spawned subprocesses.

Process finished with exit code 0

当我不使用独家新闻运行这个例子时，我得到了几乎完美的结果：

/home/chef/.venv/neuro/bin/python /home/chef/dev/projekte/NeuroEvolution-CTRNN_new/random_test.py
/home/chef/.venv/neuro/lib/python3.8/site-packages/gym/logger.py:30: UserWarning: WARN: Box bound precision lowered by casting to float32
  warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))
/home/chef/.venv/neuro/lib/python3.8/site-packages/scoop/fallbacks.py:38: RuntimeWarning: SCOOP was not started properly.
Be sure to start your program with the '-m scoop' parameter. You can find further information in the documentation.
Your map call has been replaced by the builtin serial Python map().
  warnings.warn(
not equal :(
equal, yay
equal, yay
equal, yay
equal, yay
equal, yay
equal, yay
equal, yay
equal, yay
equal, yay
equal, yay
equal, yay
equal, yay
equal, yay
equal, yay
equal, yay
equal, yay
equal, yay

Process finished with exit code 0

Answer 1

我可以通过将健身房的创建移到do-function中来“解决”它。

完全更正后的代码将如下所示：

import random
import numpy as np
from scoop import futures
import gym


def do(it):
    env = gym.make("BipedalWalker-v3")
    random.seed(it)
    np.random.seed(it)
    env.seed(it)
    env.action_space.seed(it)
    env.reset()
    observations = []
    for i in range(3):
        while True:
            action = env.action_space.sample()
            ob, reward, done, _ = env.step(action)
            observations.append(ob)
            if done:
                break
    return observations


if __name__ == "__main__":
    maxit = 20
    results1 = futures.map(do, range(2, maxit))
    results2 = futures.map(do, range(2, maxit))
    for a,b in zip(results1, results2):
        if np.array_equiv(a, b):
            print("equal, yay")
        else:
            print("not equal :(")