强化学习笔记5-Python/OpenAI/TensorFlow/ROS-阶段复习

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zhangrelay 发表于 2021/07/15 03:40:56 2021/07/15
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【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:

  1. 基础知识https://blog.csdn.net/zhangrelay/article/details/91361113
  2. 程序指令https://blog.csdn.net/zhangrelay/article/details/91414600
  3. 规划博弈https://blog.csdn.net/zhangrelay/article/details/91867331
  4. 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:

  1. 安装配置https://blog.csdn.net/zhangrelay/article/details/89702997
  2. 环境构建https://blog.csdn.net/zhangrelay/article/details/89817010
  3. 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:


      #!/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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