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https://github.com/shunzh/rl-texplore-ros-pkg

Reinforcement Learning Framework and Repository
https://github.com/shunzh/rl-texplore-ros-pkg

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Reinforcement Learning Framework and Repository

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README 

        

                          rl-texplore-ros-pkg

               Reinforcement Learning Framework and Repository



                             Todd Hester

                             4 May 2011



The stacks directory contains a ROS stack for reinforcement learning (RL). The

code in this repository provides agents, environments, and multiple ways for

them to communicate (through ROS messages, or by including the agent and 

environment libraries). There are 5 packages in the repository:



  rl_common:     Some files that are common to both agents and environments.

  rl_msgs:       Definitions of ROS messages for agents and envs to communicate.

  rl_agent:      A library of some RL agents including Q-Learning and TEXPLORE.

  rl_env:        A library of some RL environments such as Taxi and Fuel World.

  rl_experiment: Code to run some RL experiments without ROS message passing.

 

Documentation for these packages is here:



  http://www.ros.org/wiki/reinforcement_learning



And there is a more thorough tutorial here:

 

  http://www.ros.org/wiki/reinforcement_learning/Tutorials/Reinforcement%20Learning%20Tutorial



Working with the ROS build system is difficult at first.  Expect a

steep learning curve.  Working through some of the ROS tutorials is

definitely worthwhile when starting out.  Documentation:



  http://www.ros.org/wiki/ROS.



The interfaces themselves are quite good.  So are the visualization

and debugging tools.  And, there is an impressive amount of useful

robotics software already available in the ROS package system.



Directions:



 (1) Check out a copy of the RL code base with ROS interfaces



     Our packages have their own Google code project tree.  Check

     out the trunk in ~/svn/rl-texplore-ros-pkg:



     $ cd

     $ mkdir svn

     $ cd ~/svn

     $ svn co http://rl-texplore-ros-pkg.googlecode.com/svn/trunk rl-texplore-ros-pkg



     This creates a copy of the source tree without commit access.



     To commit changes yourself, create a google account and e-mail

     the project owners requesting commit access.  Then, check out an

     editable copy following these directions:



       http://code.google.com/p/rl-texplore-ros-pkg/source/checkout



 (2) Install ROS as in http://www.ros.org/wiki/ROS/Installation



     This is time-consuming, but not especially difficult.  Be careful

     to follow the directions exactly.  It works fine on Ubuntu and

     can be made to work for Mac OSX with some effort.  Windows will

     probably not work, but there are future plans to support it.



     Add the ROS environment setup to the end of your .bashrc:



     $ echo "source ~/ros/setup.sh" >> ~/.bashrc



     Append our packages as well as the ROS ones (this example does it

     from the shell, or you can use any editor):



     $ cat <<'EOF' >> ~/.bashrc

     export ROS_PACKAGE_PATH=${ROS_PACKAGE_PATH}:~/svn/rl-texplore-ros-pkg

     EOF



 (3) Log out and log back in.



 (4) Try to run some ROS commands:



     $ rospack find rl_agent

     $ roscd rl_agent

     $ rosmake rl_agent



     This should compile cleanly.



 (5) Try to run an RL experiment. First compile the code:



     $ rosmake rl_agent

     $ rosmake rl_env



     Now we want to run the code to run our experiment. We need to start 

     both an agent and an environment, and they will interact by passing 

     rl_msg messages back and forth. Open three tabs in your terminal. Run

     each of these commands in a different tab:

     

     $ roscore

     $ rosrun rl_agent agent --agent qlearner

     $ rosrun rl_env env --env taxi



     The second line starts an Q-Learner agent. There are other options to 

     start different agents. The third line starts a taxi environment. There 

     are other options here as well to start different environments. Options 

     for both of these can be seen with the -h option. In the agent window, 

     you'll see it print out the sum of rewards at the end of each episode.



 (6) ROS has some nice tools for plotting and recording data. To look at the

     messages being passed during the experiment, type:



     $ rostopic list



     This prints a list of the current messages to the screen. Let's view a 

     particular message:



     $ rostopic echo /rl_env/rl_state_reward



     Here we can see the contents of the state reward message being sent by

     the environment, which contains the current state vector, the reward,

     and a boolean of whether this is a terminal state or not.



     Another thing we can do using ros is make live plots. To make a live plot

     of reward per episode, type:



     $ rqt_plot /rl_agent/rl_experiment_info/episode_reward



     One more thing that can be done is to record all the messages being passed

     in order to view, replay, or plot them later. To record all the messages

     sent by the agent, type:



     $ rosbag record /rl_agent/rl_action /rl_agent/rl_experiment_info



     Now we can play them back in place of an agent, and just have the saved 

     messages control the agent in the environment. Kill the agent, and let's

     play back the recorded actions:



     $ rosbag play 



 (7) Finally, we can also run experiments without passing ROS messages using

     the rl_experiment package. It includes both the agent and env libraries

     directly, instead of doing message passing. First, compile this package:



     $ rosmake rl_experiment



     To run an experiment, we pass an agent and an environment on the 

     command line, and we can also pass other options. Once it starts running,

     it will print out the sum of rewards per episode for episodic tasks, or 

     the per step rewards for non-episodic tasks. Let's try running R-Max with

     M = 1 on deterministic Fuel World:



     $ rosrun rl_experiment experiment --agent rmax --m 1 --env fuelworld --deterministic