Automatic Programming Of Behavior-Based Robots Using Reinforcement Learning

Abstract

This paper describes a general approach for automatically programming a behavior-based robot. New behaviors are learned by trial and error using a performance feedback function as reinforcement. Two algorithms for behavior learning are described that combine techniques for propagating reinforcement values temporally across actions and spatially across states. A behavior-based robot called OBELIX (see Figure 1) is described that learns several component behaviors in an example task involving pushing boxes. An experimental study using the robot suggests two conclusions. One, the learning techniques are able to learn the individual behaviors, sometimes outperforming a hand-coded program. Two, using a behavior-based architecture is better than using a monolithic architecture for learning the box pushing task.