强化学习笔记1-Python/OpenAI/TensorFlow/ROS-基础知识

概念：

机器学习分支之一强化学习，学习通过与环境交互进行，是一种目标导向的方法。

不告知学习者应采用行为，但其行为对于奖励惩罚，从行为后果学习。

机器人避开障碍物案例：

靠近障碍物-10分，远离障碍物+10分。

智能体自己探索获取优良奖励的各自行为，包括如下步骤：

1. 智能体执行行为与环境交互
2. 行为执行后，智能体从一个状态转移至另一个状态
3. 依据行为获得相应的奖励或惩罚
4. 智能体理解正面和反面的行为效果
5. 获取更多奖励，避免惩罚，调整策略进行试错学习。

需要对比，理解和掌握强化学习与其他机器学习的差异，在机器人中的应用前景。

强化学习元素：智能体，策略函数，值函数，模型等。

环境类型：确定，随机，完全可观测，部分可观测，离散，连续，情景序列，非情景序列，单智能体，多智能体。

强化学习平台：OpenAI Gym/Universe/DeepMind Lab/RL-Glue/Rroject Malmo/VizDoom等。

强化学习应用：教育！医疗！健康！制造业！管理！金融！细分行业：自然语言处理/计算机视觉等。

参考文献：

• https://www.cs.ubc.ca/~murphyk/Bayes/pomdp.html
• https://morvanzhou.github.io/
• https://github.com/sudharsan13296/Hands-On-Reinforcement-Learning-With-Python

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配置：

安装配置Anaconda/Docker/OpenAI Gym/TensorFlow。

由于涉及系统环境，版本配置各不相同，自行查阅资料配置即可。

常用命令如下：

bash/conda create/source activate/apt install/docker/pip3 install gym/universe/等。

上述全部配置完成后，测试OpenAI Gym和OpenAI Universe。

*.ipynb文档查看：ipython notebook或jupyter notebook

Gym案例：

倒立摆案例：

示例代码

import gym
env = gym.make('CartPole-v0')
env.reset()
for _ in range(1000):
    env.render()
    env.step(env.action_space.sample())

关于这个代码更多内容，参考链接：

• https://blog.csdn.net/ZhangRelay/article/details/89325679

查看gym全部支持的环境。

from gym import envs
print(envs.registry.all())

赛车示例：

import gym
env = gym.make('CarRacing-v0')
env.reset()
for _ in range(1000):
    env.render()
    env.step(env.action_space.sample())

足式机器人：

import gym
env = gym.make('BipedalWalker-v2')
for episode in range(100):
    observation = env.reset()
    # Render the environment on each step 
    for i in range(10000):
        env.render() 
        # we choose action by sampling random action from environment's action space. Every environment has
        # some action space which contains the all possible valid actions and observations,
        action = env.action_space.sample()
        # Then for each step, we will record the observation, reward, done, info
        observation, reward, done, info = env.step(action)  
   # When done is true, we print the time steps taken for the episode and break the current episode.
    if done:
        print("{} timesteps taken for the Episode".format(i+1))
        break

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flash游戏环境示例：

import gym
import universe 
import random

env = gym.make('flashgames.NeonRace-v0')
env.configure(remotes=1) 
observation_n = env.reset()

# Move left
left = [('KeyEvent', 'ArrowUp', True), ('KeyEvent', 'ArrowLeft', True),
         ('KeyEvent', 'ArrowRight', False)]

# Move right
right = [('KeyEvent', 'ArrowUp', True), ('KeyEvent', 'ArrowLeft', False),
         ('KeyEvent', 'ArrowRight', True)]

# Move forward

forward = [('KeyEvent', 'ArrowUp', True), ('KeyEvent', 'ArrowRight', False),
            ('KeyEvent', 'ArrowLeft', False), ('KeyEvent', 'n', True)]

# We use turn variable for deciding whether to turn or not
turn = 0

# We store all the rewards in rewards list
rewards = []

# we will use buffer as some kind of threshold
buffer_size = 100

# We set our initial action has forward i.e our car moves just forward without making any turns
action = forward  

while True:
    turn -= 1
    
    # Let us say initially we take no turn and move forward.
    # First, We will check the value of turn, if it is less than 0
    # then there is no necessity for turning and we just move forward
    
    if turn <= 0:
        action = forward
        turn = 0
    
    action_n = [action for ob in observation_n]
    
    # Then we use env.step() to perform an action (moving forward for now) one-time step
    
    observation_n, reward_n, done_n, info = env.step(action_n)
    
    # store the rewards in the rewards list
    rewards += [reward_n[0]]
     
    # We will generate some random number and if it is less than 0.5 then we will take right, else
    # we will take left and we will store all the rewards obtained by performing each action and
    # based on our rewards we will learn which direction is the best over several timesteps. 
    
    if len(rewards) >= buffer_size:
        mean = sum(rewards)/len(rewards)
        
        if mean == 0:
            turn = 20
            if random.random() < 0.5:
                 action = right
            else:
                action = left
        rewards = []
        
    env.render()

    

部分测试如下（多次测试）：

 

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