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Overcoming incorrect knowledge in plan-based reward shaping

Published online by Cambridge University Press:  11 February 2016

Kyriakos Efthymiadis ,
Sam Devlin  and
Daniel Kudenko
Kyriakos Efthymiadis
Affiliation:
Department of Computer Science, Deramore Lane, University of York, Heslington, York, YO10 5GH, UK e-mail: [email protected], [email protected], [email protected]
Sam Devlin
Affiliation:
Department of Computer Science, Deramore Lane, University of York, Heslington, York, YO10 5GH, UK e-mail: [email protected], [email protected], [email protected]
Daniel Kudenko
Affiliation:
Department of Computer Science, Deramore Lane, University of York, Heslington, York, YO10 5GH, UK e-mail: [email protected], [email protected], [email protected]
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Abstract

Reward shaping has been shown to significantly improve an agent’s performance in reinforcement learning. Plan-based reward shaping is a successful approach in which a STRIPS plan is used in order to guide the agent to the optimal behaviour. However, if the provided knowledge is wrong, it has been shown the agent will take longer to learn the optimal policy. Previously, in some cases, it was better to ignore all prior knowledge despite it only being partially incorrect.

This paper introduces a novel use of knowledge revision to overcome incorrect domain knowledge when provided to an agent receiving plan-based reward shaping. Empirical results show that an agent using this method can outperform the previous agent receiving plan-based reward shaping without knowledge revision.

Type
Articles
Information
The Knowledge Engineering Review , Volume 31 , Issue 1: Adaptive Learning Agents , January 2016 , pp. 31 - 43
Copyright
© Cambridge University Press, 2016 

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