到目前为止，已经完成了4节课程的学习，侧重OpenAI，分别如下：

1. 1. 
      
2. 基础知识：​​​​
3. 程序指令：​​​​
4. 规划博弈：​​​​
5. 时间差分：​​​​
   1. 
      

这时候，再重新看之前博文，侧重ROS，分别如下：

1. 1. 
      
2. 安装配置：​​​​
3. 环境构建：​​​​
4. 深度学习：​​​​
   1. 
      

通过上面一系列探索学习，就能够完全掌握人工智能学工具（OpenAI）和机器人学工具（ROS）。

理解如下环境中，Q学习和SARSA差异：

Q学习-circuit2_turtlebot_lidar_qlearn.py：

#!/usr/bin/env python
import gym
from gym import wrappers
import gym_gazebo
import time
import numpy
import random
import time

import qlearn
import liveplot

def render():
    render_skip = 0 #Skip first X episodes.
    render_interval = 50 #Show render Every Y episodes.
    render_episodes = 10 #Show Z episodes every rendering.

    if (x%render_interval == 0) and (x != 0) and (x > render_skip):
        env.render()
    elif ((x-render_episodes)%render_interval == 0) and (x != 0) and (x > render_skip) and (render_episodes < x):
        env.render(close=True)

if __name__ == '__main__':

    env = gym.make('GazeboCircuit2TurtlebotLidar-v0')

    outdir = '/tmp/gazebo_gym_experiments'
    env = gym.wrappers.Monitor(env, outdir, force=True)
    plotter = liveplot.LivePlot(outdir)

    last_time_steps = numpy.ndarray(0)

    qlearn = qlearn.QLearn(actions=range(env.action_space.n),
                    alpha=0.2, gamma=0.8, epsilon=0.9)

    initial_epsilon = qlearn.epsilon

    epsilon_discount = 0.9986

    start_time = time.time()
    total_episodes = 10000
    highest_reward = 0

    for x in range(total_episodes):
        done = False

        cumulated_reward = 0 #Should going forward give more reward then L/R ?

        observation = env.reset()

        if qlearn.epsilon > 0.05:
            qlearn.epsilon *= epsilon_discount

        #render() #defined above, not env.render()

        state = ''.join(map(str, observation))

        for i in range(1500):

            # Pick an action based on the current state
            action = qlearn.chooseAction(state)

            # Execute the action and get feedback
            observation, reward, done, info = env.step(action)
            cumulated_reward += reward

            if highest_reward < cumulated_reward:
                highest_reward = cumulated_reward

            nextState = ''.join(map(str, observation))

            qlearn.learn(state, action, reward, nextState)

            env._flush(force=True)

            if not(done):
                state = nextState
            else:
                last_time_steps = numpy.append(last_time_steps, [int(i + 1)])
                break

        if x%100==0:
            plotter.plot(env)

        m, s = divmod(int(time.time() - start_time), 60)
        h, m = divmod(m, 60)
        print ("EP: "+str(x+1)+" - [alpha: "+str(round(qlearn.alpha,2))+" - gamma: "+str(round(qlearn.gamma,2))+" - epsilon: "+str(round(qlearn.epsilon,2))+"] - Reward: "+str(cumulated_reward)+"     Time: %d:%02d:%02d" % (h, m, s))

    #Github table content
    print ("\n|"+str(total_episodes)+"|"+str(qlearn.alpha)+"|"+str(qlearn.gamma)+"|"+str(initial_epsilon)+"*"+str(epsilon_discount)+"|"+str(highest_reward)+"| PICTURE |")

    l = last_time_steps.tolist()
    l.sort()

    #print("Parameters: a="+str)
    print("Overall score: {:0.2f}".format(last_time_steps.mean()))
    print("Best 100 score: {:0.2f}".format(reduce(lambda x, y: x + y, l[-100:]) / len(l[-100:])))

    env.close()

SARSA-circuit2_turtlebot_lidar_sarsa.py：

#!/usr/bin/env python
import gym
from gym import wrappers
import gym_gazebo
import time
import numpy
import random
import time

import liveplot
import sarsa


if __name__ == '__main__':

    env = gym.make('GazeboCircuit2TurtlebotLidar-v0')

    outdir = '/tmp/gazebo_gym_experiments'
    env = gym.wrappers.Monitor(env, outdir, force=True)
    plotter = liveplot.LivePlot(outdir)

    last_time_steps = numpy.ndarray(0)

    sarsa = sarsa.Sarsa(actions=range(env.action_space.n),
                    epsilon=0.9, alpha=0.2, gamma=0.9)

    initial_epsilon = sarsa.epsilon

    epsilon_discount = 0.9986

    start_time = time.time()
    total_episodes = 10000
    highest_reward = 0

    for x in range(total_episodes):
        done = False

        cumulated_reward = 0 #Should going forward give more reward then L/R ?

        observation = env.reset()

        if sarsa.epsilon > 0.05:
            sarsa.epsilon *= epsilon_discount

        #render() #defined above, not env.render()

        state = ''.join(map(str, observation))

        for i in range(1500):

            # Pick an action based on the current state
            action = sarsa.chooseAction(state)

            # Execute the action and get feedback
            observation, reward, done, info = env.step(action)
            cumulated_reward += reward

            if highest_reward < cumulated_reward:
                highest_reward = cumulated_reward

            nextState = ''.join(map(str, observation))
            nextAction = sarsa.chooseAction(nextState)

            #sarsa.learn(state, action, reward, nextState)
            sarsa.learn(state, action, reward, nextState, nextAction)

            env._flush(force=True)

            if not(done):
                state = nextState
            else:
                last_time_steps = numpy.append(last_time_steps, [int(i + 1)])
                break

        if x%100==0:
            plotter.plot(env)

        m, s = divmod(int(time.time() - start_time), 60)
        h, m = divmod(m, 60)
        print ("EP: "+str(x+1)+" - [alpha: "+str(round(sarsa.alpha,2))+" - gamma: "+str(round(sarsa.gamma,2))+" - epsilon: "+str(round(sarsa.epsilon,2))+"] - Reward: "+str(cumulated_reward)+"     Time: %d:%02d:%02d" % (h, m, s))

    #Github table content
    print ("\n|"+str(total_episodes)+"|"+str(sarsa.alpha)+"|"+str(sarsa.gamma)+"|"+str(initial_epsilon)+"*"+str(epsilon_discount)+"|"+str(highest_reward)+"| PICTURE |")

    l = last_time_steps.tolist()
    l.sort()

    #print("Parameters: a="+str)
    print("Overall score: {:0.2f}".format(last_time_steps.mean()))
    print("Best 100 score: {:0.2f}".format(reduce(lambda x, y: x + y, l[-100:]) / len(l[-100:])))

    env.close()

复习：时间差分​​

其中案例出租车demo与上面turtlebot-demo，理解并掌握ROS和OpenAI这两大工具最基本的应用。