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Plan-based reward shaping for multi-agent reinforcement learning

Published online by Cambridge University Press:  11 February 2016

Sam Devlin  and
Daniel Kudenko
Sam Devlin
Affiliation:
Department of Computer Science, University of York, York, YO10 5GH, England e-mail: sam.devlin@york.ac.uk, daniel.kudenko@york.ac.uk
Daniel Kudenko
Affiliation:
Department of Computer Science, University of York, York, YO10 5GH, England e-mail: sam.devlin@york.ac.uk, daniel.kudenko@york.ac.uk
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Abstract

Recent theoretical results have justified the use of potential-based reward shaping as a way to improve the performance of multi-agent reinforcement learning (MARL). However, the question remains of how to generate a useful potential function.

Previous research demonstrated the use of STRIPS operator knowledge to automatically generate a potential function for single-agent reinforcement learning. Following up on this work, we investigate the use of STRIPS planning knowledge in the context of MARL.

Our results show that a potential function based on joint or individual plan knowledge can significantly improve MARL performance compared with no shaping. In addition, we investigate the limitations of individual plan knowledge as a source of reward shaping in cases where the combination of individual agent plans causes conflict.

Type
Articles
Information
The Knowledge Engineering Review , Volume 31 , Issue 1: Adaptive Learning Agents , January 2016 , pp. 44 - 58
Copyright
© Cambridge University Press, 2016 

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