最近使用gym提供的小游戏做强化学习DQN算法的研究，首先就是要获取游戏截图，并且对截图做一些预处理。

screen = env.render(mode='rgb_array')

上述代码是将游戏截图转换为一个rgb的数组(numpy.ndarray)，由变量screen保存。其中mode一共有三种模式：human, rgb_array和ansi。在gym.core中找到官方说明，如下所示：

    def render(self, mode="human"):
        """Renders the environment.

        The set of supported modes varies per environment. (And some
        environments do not support rendering at all.) By convention,
        if mode is:

        - human: render to the current display or terminal and
          return nothing. Usually for human consumption.
        - rgb_array: Return an numpy.ndarray with shape (x, y, 3),
          representing RGB values for an x-by-y pixel image, suitable
          for turning into a video.
        - ansi: Return a string (str) or StringIO.StringIO containing a
          terminal-style text representation. The text can include newlines
          and ANSI escape sequences (e.g. for colors).

        Note:
            Make sure that your class's metadata 'render.modes' key includes
              the list of supported modes. It's recommended to call super()
              in implementations to use the functionality of this method.

        Args:
            mode (str): the mode to render with

        Example:

        class MyEnv(Env):
            metadata = {'render.modes': ['human', 'rgb_array']}

            def render(self, mode='human'):
                if mode == 'rgb_array':
                    return np.array(...) # return RGB frame suitable for video
                elif mode == 'human':
                    ... # pop up a window and render
                else:
                    super(MyEnv, self).render(mode=mode) # just raise an exception
        """
        raise NotImplementedError

一般用的比较多的就是rgb_array，可以对图像进行修改。human会返回一个bool变量，主要是用来在屏幕上显示当前的游戏图像。ansi目前还不太了解相关具体的应用。