From Brown University Robotics
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Revision as of 03:09, 10 August 2010
Reinforcement learning (RL) is a sub-area of machine learning concerned with how an agent should select actions given its environment and often cites robotics as a potential application area. While some researchers have bridged the gap and used RL algorithms in robot applications, most RL experiments happen in simulation and are never ported over to the robotics due to the difficulty of programming and maintaining a robot. Additionally individuals in the RL community use their own software frameworks for evaluating and creating learning techniques. RL-Glue is a standard interface that allows RL researchers to share agent, environments and experiment programs together. Robotics has suffered from similar problems where labs have primarily created their own infrastructure and evaluation across different techniques has become difficult if not impossible. ROS is a large sophisticated research tool that is currently be used by many roboticists world-wide.
We introduce rosglue, framework that allows robots running ROS to be environments for RL-Glue agents. Our hope is that this may lead to increased communication between the fields and open further collaborations.
Short Primer on ROS
ROS is an open-source robot middle ware system. It provides many services including hardware abstraction, low-level device control, implementations for commonly used functionality, and message-passing. If you're familiar with ROS, feel free to skim or skip this section. If you've never heard of ROS before or know very little about it you can learn more by checking out the tutorials and documentation on http://www.ros.org/wiki/ However, our goal is to allow you to use at least some robots running ROS with as little understanding of this as possible.
Topics and Services
Perhaps the most important thing to understand about ROS is how it exposes the functionality of the robot. This happens in one of two ways, as a topic or as a service. Both services and topics can be used for observing the robots environment or for performing control.
Topics are an asynchronous communication of streams of objects. A process can publish topics and other processes may subscribe to these topics and use the data as they wish without directly communicating to the publisher process.
Services are a synchronous communication system and are much like function calls in many programming languages, they take in arguments and return responses. Services, under ROS, will always return an object which can be arbitrarily complex.
Short Primer on RL-Glue
RL-Glue provides a standard interface for the three major components of an RL system: the agent, the environment, and the experiment. Much like with ROS you're familiar with RL-Glue, feel free to skim or skip this section. If you've never heard of RL-Glue before or know very little about it you can learn more by checking out http://glue.rl-community.org/wiki/Main_Page
In order to program in RL-Glue developers download a codec for the language of the user's choice, currently C/C++, Java, Lisp, Matlab, and Python are supported. The RL-Glue interface is a series of functions that are defined by the codec.
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These functions define a low level protocol for connecting agents, envirionments and experiments. The developers fill in the functions with the desired functionality and then rl_glue passes the messages between the agent, environment and experiment. Users can also use the RL-Library , an open-source collection RL-Glue compatible agent, environments, and experiments.
rosglue is designed to be a bridge between RL-Glue and ROS. As pictured in the figure, rosglue treats a robot running ROS as an RL-Glue environment.
A user using rosglue, defines the robotics envirionment through a yaml file.