Automatic Programming of Behavior-Based Robots Using Reinforcement Learning

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 Q learning, a well-known scheme for propagating reinforcement values temporally across actions, with statistical clustering and Hamming distance. two ways of propagating reinforcement values spatially across states. A real behavior-based robot called OBELIX is described that learns several component behaviors in an example task involving pushing boxes. A simulator for the box pushing task is also used to gather data on the learning techniques. A detailed experimental study using the real robot and the simulator suggests two conclusions. (1) The learning techniques are able to learn the individual behaviors, sometimes outperforming a handcoded program. (2) Using a behavior-based architecture speeds up reinforcement learning by converting the problem of learning a complex task into that of learning a simpler set of special-purpose reactive subtasks.