Reinforcement Learning in Practice

对网上各处寻找reinforcement learning的实践环节感到无力，尤其是对open ai gym的介绍上。cnblogs、github、csdn、zhuanlan.zhihu、公众号、google、youtube等等，都非常的零碎。公开课上主要是理论，对代码实现讲的很少。对于像我这样的新手来说简直两眼一抹黑，无从下手的感觉。

目前我自己在用的学习材料：

1，官方文档

https://gym.openai.com/docs/

2，知乎专栏

https://zhuanlan.zhihu.com/reinforce（等等）

3，github

https://github.com/openai/gym

https://github.com/dennybritz/reinforcement-learning

4，git blog、youtube

莫烦python

在此处作为一个整理工具，放一些学习材料中的重要知识点和我自己的实现。大致的计划是，先实现一些Silver课程上的基本算法或者gym里的案例控制，后面再学deep learning和DRL，这个顺序。

4/9/2018 实现iterative policy evaluation用于grid world

学习材料：

https://zhuanlan.zhihu.com/p/28084990

我的代码：（我的gridworld问题和专栏里面有点不一样，我这里出口只有右下角一个，并且我用的是table和矩阵运算的形式来实现的）

https://github.com/ysgclight/RL_Homework/blob/master/4x4gridworld.py

4/10/2018 从零开始学习gym的ABC

1，重要知识点：

https://zhuanlan.zhihu.com/p/28086233 

 

 

一个小问题：env和space到底有什么区别啊？

2，

mountain_car.py中对于观测空间和行为空间描述

core.py中有一以下重要的class

class Env(object):
    """The main OpenAI Gym class. It encapsulates an environment with
    arbitrary behind-the-scenes dynamics. An environment can be
    partially or fully observed.

    The main API methods that users of this class need to know are:

        step
        reset
        render
        close
        seed

    And set the following attributes:

        action_space: The Space object corresponding to valid actions
        observation_space: The Space object corresponding to valid observations
        reward_range: A tuple corresponding to the min and max possible rewards

    Note: a default reward range set to [-inf,+inf] already exists. Set it if you want a narrower range.

    The methods are accessed publicly as "step", "reset", etc.. The
    non-underscored versions are wrapper methods to which we may add
    functionality over time.
    """

　　def step(self, action):
        """Run one timestep of the environment's dynamics. When end of
        episode is reached, you are responsible for calling `reset()`
        to reset this environment's state.

        Accepts an action and returns a tuple (observation, reward, done, info).

        Args:
            action (object): an action provided by the environment

        Returns:
            observation (object): agent's observation of the current environment
            reward (float) : amount of reward returned after previous action
            done (boolean): whether the episode has ended, in which case further step() calls will return undefined results
            info (dict): contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)
        """
        raise NotImplementedError

    def reset(self):
        """Resets the state of the environment and returns an initial observation.

        Returns: observation (object): the initial observation of the
            space.
        """
        raise NotImplementedError

    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
            close (bool): close all open renderings

        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 is 'human':
                    ... # pop up a window and render
                else:
                    super(MyEnv, self).render(mode=mode) # just raise an exception
        """
        raise NotImplementedError

class Space(object):
    """Defines the observation and action spaces, so you can write generic
    code that applies to any Env. For example, you can choose a random
    action.
    """

 3，gym官方文件

envs定义游戏类型

spaces定义action和observation的抽象数据空间

4，gym envs二次开发

https://zhuanlan.zhihu.com/p/26985029

第零步，把gym下载位置保存到/.bashrs为PYTHONPATH这个环境变量，否则下面的操作会报错

第一步，在gym/gym/envs/classic_control下面写入新的env子类

　　至少包括以下函数：__init__(self)，step，reset，render

这个就是最坑的地方，调试了一晚上都没完全调试出来。

其中一个因素是gym改版了，而这个教程里的代码是旧的，所以运行会报错！

注意，我进行了以下修改：

*添加register的时候，括号的内容加了tab缩进

*GridEnv里面的__init__函数，加一个self.seed()，并且copy了cartpole.py里的def seed函数定义。开头import了seeding进来，参考cartpole那样

*registration.py里面把174-191行的def patch_deprecated_methods(env)屏蔽了

*grid_mdp.py所有的主要函数都去掉了开头的_符号

*terminal退出当前的python命令，再重进，就可以在terminal调用import gym，定义env=新加入的GridEnv-v0，然后reset，render执行，不会报错了，render也生效了。但是quit之后会提示TypeError: 'NoneType' object is not iterable。

*在其他文件下调用执行相同的程序，不显示render效果，只有上面的那个typeerror。

*在vscode里执行官方代码的时候

import gym

env = gym.make('CartPole-v0')

env.reset()

for _ in range(1000):

　　env.render()

　　env.step(env.action_space.sample()) # take a random action

 

也会弹出相应的错误，但是render效果会显示一段时间。改成gridworld之后 ，render效果一闪而过，完全看不清楚

需要在env.render和env.step之间加入一个time.sleep(4)强制停下来（当然，要import time）

 

 

 

https://zhuanlan.zhihu.com/p/36506567