Team Learning From Human Demonstration With Coordination Confidence
Document Type
Article
Publication Date
1-1-2019
School
Computing Sciences and Computer Engineering
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.
Publication Title
Knowledge Engineering Review
Volume
34
Recommended Citation
Banerjee, B.,
Vittanala, S.,
Taylor, M.
(2019). Team Learning From Human Demonstration With Coordination Confidence. Knowledge Engineering Review, 34.
Available at: https://aquila.usm.edu/fac_pubs/19143