reinforcement_learning repository

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Last Updated 2015-09-06
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Name Version
rl_agent 0.0.0
rl_common 0.0.0
rl_env 0.0.0
rl_experiment 0.0.0
rl_msgs 0.0.0


               Reinforcement Learning Framework and Repository

                             Todd Hester
                             4 May 2011

                           July 2015 Update
          The indigo-devel branch of this repository contains a catkinized
          version of the original codebase. If you are using ROS Groovy or
          newer, you should check out this version.

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:

And there is a more thorough tutorial here:

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:

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.


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

     Our packages have their own Github project.  Check
     out the trunk of rl-texplore-ros-pkg:

     $ git clone

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

     To commit changes yourself, create a github account and e-mail
     the project owners requesting commit access.  

 (2) Install ROS as in

     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/" >> ~/.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

 (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:

     $ rxplot /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 <bagfile>

 (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


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