@inproceedings{25c01466cbdb410bb1b8d400d37d6834,
title = "Opponent modelling for reinforcement learning in multi-objective normal form games",
abstract = "In this paper, we investigate the effects of opponent modelling on multi-objective multi-agent interactions with non-linear utilities. Specifically, we consider multi-objective normal form games (MONFGs) with non-linear utility functions under the scalarised expected returns optimisation criterion. We contribute a novel actor-critic formulation to allow reinforcement learning of mixed strategies in this setting, along with an extension that incorporates opponent policy reconstruction using conditional action frequencies. Our empirical results demonstrate that opponent modelling can drastically alter the learning dynamics in this setting.",
keywords = "Game theory, Multi-agent systems, Multi-objective decision making, Nash equilibrium, Opponent modelling, Reinforcement learning",
author = "Yijie Zhang and Roxana R{\u a}dulescu and Patrick Mannion and Roijers, \{Diederik M.\} and Ann Now{\'e}",
note = "Publisher Copyright: {\textcopyright} 2020 International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). All rights reserved.; 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020 ; Conference date: 19-05-2020",
year = "2020",
language = "English",
series = "Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS",
publisher = "International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)",
pages = "2080--2082",
editor = "Bo An and \{El Fallah Seghrouchni\}, Amal and Gita Sukthankar",
booktitle = "Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020",
}