E-HBA: Using Action Policies for Expert Advice and Agent Typification
Abstract
Past research has studied two approaches to utilise predefined policy sets in repeated interactions: as experts, to dictate our own actions, and as types, to characterise the behaviour of other agents. In this work, we bring these complementary views together in the form of a novel meta-algorithm, called Expert-HBA (E-HBA), which can be applied to any expert algorithm that considers the average (or total) payoff an expert has yielded in the past. E-HBA gradually mixes the past payoff with a predicted future payoff, which is computed using the type-based characterisation. We present results from a comprehensive set of repeated matrix games, comparing the performance of several well-known expert algorithms with and without the aid of E-HBA. Our results show that E-HBA has the potential to significantly improve the performance of expert algorithms.
Cite
@article{arxiv.1907.09810,
title = {E-HBA: Using Action Policies for Expert Advice and Agent Typification},
author = {Stefano V. Albrecht and Jacob W. Crandall and Subramanian Ramamoorthy},
journal= {arXiv preprint arXiv:1907.09810},
year = {2019}
}
Comments
Proceedings of the Second Workshop on Multiagent Interaction without Prior Coordination (MIPC), 2015