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Comparative Evaluation of Multiagent Learning Algorithms in a Diverse Set of Ad Hoc Team Problems by Albrecht, Stefano V.; Ramamoorthy, Subramanian is a scholarly article available to read on EtoBox.

What is Comparative Evaluation of Multiagent Learning Algorithms in a Diverse Set of Ad Hoc Team Problems about?

This paper is concerned with evaluating different multiagent learning (MAL) algorithms in problems where individual agents may be heterogenous, in the sense of utilizing different learning strategies, without the opportunity for prior agreements or information regarding coordination. Such a situation arises in ad hoc team problems, a model of many practical multiagent systems applications. Prior work in multiagent learning has often been focussed on homogeneous groups of agents, meaning that all agents were identical and a priori aware of this fact. Also, those algorithms that are specifically designed for ad hoc team problems are typically evaluated in teams of agents with fixed behaviours, as opposed to agents which are adapting their behaviours. In this work, we empirically evaluate five MAL algorithms, representing major approaches to multiagent learning but originally developed with the homogeneous setting in mind, to understand their behaviour in a set of ad hoc team problems. All teams consist of agents which are continuously adapting their behaviours. The algorithms are evaluated with respect to a comprehensive characterisation of repeated matrix games, using performance cr

Author

Albrecht, Stefano V.; Ramamoorthy, Subramanian

Published

2019

Language

EN