Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning

Authors

  • Nicolas Anastassacos The Alan Turing Institute
  • Stephen Hailes University College London
  • Mirco Musolesi University College London

DOI:

https://doi.org/10.1609/aaai.v34i05.6190

Abstract

Social dilemmas have been widely studied to explain how humans are able to cooperate in society. Considerable effort has been invested in designing artificial agents for social dilemmas that incorporate explicit agent motivations that are chosen to favor coordinated or cooperative responses. The prevalence of this general approach points towards the importance of achieving an understanding of both an agent's internal design and external environment dynamics that facilitate cooperative behavior. In this paper, we investigate how partner selection can promote cooperative behavior between agents who are trained to maximize a purely selfish objective function. Our experiments reveal that agents trained with this dynamic learn a strategy that retaliates against defectors while promoting cooperation with other agents resulting in a prosocial society.

Downloads

Published

2020-04-03

How to Cite

Anastassacos, N., Hailes, S., & Musolesi, M. (2020). Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 7047-7054. https://doi.org/10.1609/aaai.v34i05.6190

Issue

Section

AAAI Technical Track: Multiagent Systems