Use Cases

1. Develop your own RL Task

1. Robot Environment Jetbot_robot_env.py

2. The thing you need to do in this file:

   • Open Gazebo
   • Determine how to control
   • Determine which sensor to use
   • Set virtual method for Task Environment
   • Note: All staff you need to change in the code is commented by TODO in code.

3. Details

   • Init robot envrionment from gazebo environment.

     python class JetbotRobotEnv(robot_gazebo_env.RobotGazeboEnv):

   • Choose your own controller

     python self.controllers_list = ['jetbot_joint_state_controller', 'jetbot_velocity_controller' ]

     Note:

     use this to get controller:

     $ rosservice call /jetbot_0/controller_manager/list_controllers * Change the namespace python self.robot_name_space = "jetbot_0"

     Note:

     use this to get the namespace

     $ rostopic list | grep controller

4. Change Actions

2. Start training

roslaunch jetbot_rl start_training.launch

3. Plot Training Result

Run rqt_multipolt

rosrun rqt_multiplot rqt_multiplot

Choose configure -> choose topic [openai/reward] -> choose start plot