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Team learning from human demonstration with coordination confidence

Part of: Adaptive Learning Agents 2018

Published online by Cambridge University Press:  05 November 2019

Bikramjit Banerjee ,
Syamala Vittanala  and
Matthew Edmund Taylor
Bikramjit Banerjee
Affiliation:
School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS 39406, USA; e-mail: [email protected]
Syamala Vittanala
Affiliation:
School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS 39406, USA; e-mail: [email protected]
Matthew Edmund Taylor
Affiliation:
School of Electrical Engineering & Computer Science, Washington State University, Pullman, WA 99164, USA e-mail: [email protected]
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Abstract

Among an array of techniques proposed to speed-up reinforcement learning (RL), learning from human demonstration has a proven record of success. A related technique, called Human-Agent Transfer, and its confidence-based derivatives have been successfully applied to single-agent RL. This article investigates their application to collaborative multi-agent RL problems. We show that a first-cut extension may leave room for improvement in some domains, and propose a new algorithm called coordination confidence (CC). CC analyzes the difference in perspectives between a human demonstrator (global view) and the learning agents (local view) and informs the agents’ action choices when the difference is critical and simply following the human demonstration can lead to miscoordination. We conduct experiments in three domains to investigate the performance of CC in comparison with relevant baselines.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 34 , 2019 , e12
Copyright
© Cambridge University Press, 2019 

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