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baselines中环境包装器EpisodicLifeEnv的分析

环境 分析 包装
2023-09-11 14:19:19 时间

 

如题：

class EpisodicLifeEnv(gym.Wrapper):
    def __init__(self, env):
        """Make end-of-life == end-of-episode, but only reset on true game over.
        Done by DeepMind for the DQN and co. since it helps value estimation.
        """
        gym.Wrapper.__init__(self, env)
        self.lives = 0
        self.was_real_done = True

    def step(self, action):
        obs, reward, done, info = self.env.step(action)
        self.was_real_done = done
        # check current lives, make loss of life terminal,
        # then update lives to handle bonus lives
        lives = self.env.unwrapped.ale.lives()
        if lives < self.lives and lives > 0:
            # for Qbert sometimes we stay in lives == 0 condition for a few frames
            # so it's important to keep lives > 0, so that we only reset once
            # the environment advertises done.
            done = True
        self.lives = lives
        return obs, reward, done, info

    def reset(self, **kwargs):
        """Reset only when lives are exhausted.
        This way all states are still reachable even though lives are episodic,
        and the learner need not know about any of this behind-the-scenes.
        """
        if self.was_real_done:
            obs = self.env.reset(**kwargs)
        else:
            # no-op step to advance from terminal/lost life state
            obs, _, _, _ = self.env.step(0)
        self.lives = self.env.unwrapped.ale.lives()
        return obs

 

 

EpisodicLifeEnv包装器是针对环境中有多条lives的，游戏中所剩的lives通过： lives = self.env.unwrapped.ale.lives()获得。

主要需要说明的代码为：

        if lives < self.lives and lives > 0:
            # for Qbert sometimes we stay in lives == 0 condition for a few frames
            # so it's important to keep lives > 0, so that we only reset once
            # the environment advertises done.
            done = True

根据注释可以知道，对于游戏Qbert来说当所剩lives为0的时候这时返回的done为false，也就是说还需要几帧画面后才会获得done=True的反馈，如果我们将判断条件：

        if lives < self.lives and lives > 0:

改为：

        if lives < self.lives and lives >=0:

这样，step返回的 return obs, reward, done, info 将作为一个episode的最后一帧数据来处理，并调用reset函数中的：

        else:
            # no-op step to advance from terminal/lost life state
            obs, _, _, _ = self.env.step(0)

这样，在随后的几帧数据中由于 self.was_real_done = False，而  lives = self.env.unwrapped.ale.lives()=0，会不断的循环调用reset操作。

 

 

当然针对Qbert游戏中的这种问题我们还可以使用其他的修改方式：

class EpisodicLifeEnv(gym.Wrapper):
    def __init__(self, env):
        """Make end-of-life == end-of-episode, but only reset on true game over.
        Done by DeepMind for the DQN and co. since it helps value estimation.
        """
        gym.Wrapper.__init__(self, env)
        self.lives = 0
        self.was_real_done = True

    def step(self, action):
        obs, reward, done, info = self.env.step(action)
        # self.was_real_done = done
        # check current lives, make loss of life terminal,
        # then update lives to handle bonus lives
        lives = self.env.unwrapped.ale.lives()
        if lives < self.lives:
            # for Qbert sometimes we stay in lives == 0 condition for a few frames
            # so it's important to keep lives > 0, so that we only reset once
            # the environment advertises done.
            done = True
        self.lives = lives
        return obs, reward, done, info

    def reset(self, **kwargs):
        """Reset only when lives are exhausted.
        This way all states are still reachable even though lives are episodic,
        and the learner need not know about any of this behind-the-scenes.
        """
        # if self.was_real_done:
        if self.lives == 0:
            obs = self.env.reset(**kwargs)
        else:
            # no-op step to advance from terminal/lost life state
            obs, _, _, _ = self.env.step(0)
        self.lives = self.env.unwrapped.ale.lives()
        return obs

 

 

 

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