强化学习笔记5-Python/OpenAI/TensorFlow/ROS-阶段复习
【摘要】 到目前为止，已经完成了4节课程的学习，侧重OpenAI，分别如下：
基础知识：https://blog.csdn.net/zhangrelay/article/details/91361113程序指令：https://blog.csdn.net/zhangrelay/article/details/91414600规划博弈：https://blog.csdn.net/zha...
到目前为止，已经完成了4节课程的学习，侧重OpenAI，分别如下：
- 基础知识：https://blog.csdn.net/zhangrelay/article/details/91361113
- 程序指令：https://blog.csdn.net/zhangrelay/article/details/91414600
- 规划博弈：https://blog.csdn.net/zhangrelay/article/details/91867331
- 时间差分：https://blog.csdn.net/zhangrelay/article/details/92012795
这时候，再重新看之前博文，侧重ROS，分别如下：
- 安装配置：https://blog.csdn.net/zhangrelay/article/details/89702997
- 环境构建：https://blog.csdn.net/zhangrelay/article/details/89817010
- 深度学习：https://blog.csdn.net/zhangrelay/article/details/90177162
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：
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#!/usr/bin/env python
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import gym
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from gym import wrappers
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import gym_gazebo
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import time
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import numpy
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import random
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import time
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import qlearn
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import liveplot
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def render():
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render_skip = 0 #Skip first X episodes.
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render_interval = 50 #Show render Every Y episodes.
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render_episodes = 10 #Show Z episodes every rendering.
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if (x%render_interval == 0) and (x != 0) and (x > render_skip):
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env.render()
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elif ((x-render_episodes)%render_interval == 0) and (x != 0) and (x > render_skip) and (render_episodes < x):
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env.render(close=True)
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if __name__ == '__main__':
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env = gym.make('GazeboCircuit2TurtlebotLidar-v0')
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outdir = '/tmp/gazebo_gym_experiments'
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env = gym.wrappers.Monitor(env, outdir, force=True)
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plotter = liveplot.LivePlot(outdir)
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last_time_steps = numpy.ndarray(0)
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qlearn = qlearn.QLearn(actions=range(env.action_space.n),
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alpha=0.2, gamma=0.8, epsilon=0.9)
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initial_epsilon = qlearn.epsilon
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epsilon_discount = 0.9986
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start_time = time.time()
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total_episodes = 10000
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highest_reward = 0
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for x in range(total_episodes):
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done = False
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cumulated_reward = 0 #Should going forward give more reward then L/R ?
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observation = env.reset()
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if qlearn.epsilon > 0.05:
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qlearn.epsilon *= epsilon_discount
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#render() #defined above, not env.render()
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state = ''.join(map(str, observation))
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for i in range(1500):
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# Pick an action based on the current state
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action = qlearn.chooseAction(state)
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# Execute the action and get feedback
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observation, reward, done, info = env.step(action)
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cumulated_reward += reward
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if highest_reward < cumulated_reward:
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highest_reward = cumulated_reward
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nextState = ''.join(map(str, observation))
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qlearn.learn(state, action, reward, nextState)
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env._flush(force=True)
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if not(done):
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state = nextState
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else:
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last_time_steps = numpy.append(last_time_steps, [int(i + 1)])
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break
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if x%100==0:
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plotter.plot(env)
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m, s = divmod(int(time.time() - start_time), 60)
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h, m = divmod(m, 60)
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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))
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#Github table content
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print ("\n|"+str(total_episodes)+"|"+str(qlearn.alpha)+"|"+str(qlearn.gamma)+"|"+str(initial_epsilon)+"*"+str(epsilon_discount)+"|"+str(highest_reward)+"| PICTURE |")
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l = last_time_steps.tolist()
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l.sort()
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#print("Parameters: a="+str)
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print("Overall score: {:0.2f}".format(last_time_steps.mean()))
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print("Best 100 score: {:0.2f}".format(reduce(lambda x, y: x + y, l[-100:]) / len(l[-100:])))
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env.close()
SARSA-circuit2_turtlebot_lidar_sarsa.py：
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#!/usr/bin/env python
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import gym
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from gym import wrappers
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import gym_gazebo
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import time
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import numpy
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import random
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import time
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import liveplot
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import sarsa
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if __name__ == '__main__':
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env = gym.make('GazeboCircuit2TurtlebotLidar-v0')
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outdir = '/tmp/gazebo_gym_experiments'
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env = gym.wrappers.Monitor(env, outdir, force=True)
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plotter = liveplot.LivePlot(outdir)
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last_time_steps = numpy.ndarray(0)
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sarsa = sarsa.Sarsa(actions=range(env.action_space.n),
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epsilon=0.9, alpha=0.2, gamma=0.9)
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initial_epsilon = sarsa.epsilon
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epsilon_discount = 0.9986
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start_time = time.time()
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total_episodes = 10000
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highest_reward = 0
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for x in range(total_episodes):
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done = False
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cumulated_reward = 0 #Should going forward give more reward then L/R ?
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observation = env.reset()
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if sarsa.epsilon > 0.05:
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sarsa.epsilon *= epsilon_discount
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#render() #defined above, not env.render()
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state = ''.join(map(str, observation))
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for i in range(1500):
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# Pick an action based on the current state
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action = sarsa.chooseAction(state)
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# Execute the action and get feedback
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observation, reward, done, info = env.step(action)
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cumulated_reward += reward
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if highest_reward < cumulated_reward:
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highest_reward = cumulated_reward
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nextState = ''.join(map(str, observation))
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nextAction = sarsa.chooseAction(nextState)
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#sarsa.learn(state, action, reward, nextState)
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sarsa.learn(state, action, reward, nextState, nextAction)
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env._flush(force=True)
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if not(done):
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state = nextState
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else:
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last_time_steps = numpy.append(last_time_steps, [int(i + 1)])
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break
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if x%100==0:
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plotter.plot(env)
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m, s = divmod(int(time.time() - start_time), 60)
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h, m = divmod(m, 60)
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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))
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#Github table content
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print ("\n|"+str(total_episodes)+"|"+str(sarsa.alpha)+"|"+str(sarsa.gamma)+"|"+str(initial_epsilon)+"*"+str(epsilon_discount)+"|"+str(highest_reward)+"| PICTURE |")
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l = last_time_steps.tolist()
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l.sort()
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#print("Parameters: a="+str)
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print("Overall score: {:0.2f}".format(last_time_steps.mean()))
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print("Best 100 score: {:0.2f}".format(reduce(lambda x, y: x + y, l[-100:]) / len(l[-100:])))
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env.close()
复习：时间差分https://blog.csdn.net/zhangrelay/article/details/92012795
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。
文章来源: zhangrelay.blog.csdn.net，作者：zhangrelay，版权归原作者所有，如需转载，请联系作者。
原文链接：zhangrelay.blog.csdn.net/article/details/92050001
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