Automatic Programming of Behavior-based Robots using Reinforcement Learning (1991)
| Citations: | 316 - 16 self |
BibTeX
@MISC{Mahadevan91automaticprogramming,
author = {S. Mahadevan and J. Connell and C. Sammut and R. Sutton and Temporal Phd},
title = {Automatic Programming of Behavior-based Robots using Reinforcement Learning},
year = {1991}
}
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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 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. One, the learning techniques are able to learn the individual behaviors, sometimes outperforming a handcoded program. Two, 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.
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