但是在env.action_space.sample()中发现 a1 != a2,说明 action_space.sample()不受env.seed()控制。 import gym import numpy as np env1 = gym.make('CartPole-v1') env2 = gym.make('CartPole-v1') env3 = gym.make('CartPole-v1') env1.seed(1) env2.seed(1) env3.seed(2) s1 = env...
作用：设置env.reset()是随机还是固定 验证：env.seed(number)#number为固定值 print(env.reset()）#...
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import gymnasium as gym import numpy as np for s in [0,1,2,3,4]: env=gym.make("BreakoutNoFrameskip-v4") observation, info = env.reset(seed=s) print(s, np.sum(observation)) I tried the bellowing code and found out the initial state of breakout environment is the same with di...
self._np_random, seed1 = seeding.np_random(seed) System Info macOS 10.15.7 pip 22.0.3 Python version 3.8.6 This just seems to be because ALE isn't yet updated to support the not-yet-released version of gym? Use the official releases that are compatible with one another, notmaster ...
Random Seed Feature#250 Closed RuofanKongopened this issueJul 19, 2016· 4 comments RuofanKongcommentedJul 19, 2016 Yes, envs in gym expose a seed() function for exactly this purpose e.g. >>> import gym >>> env = gym.make('CartPole-v0') >>> env.seed(0) ...
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