Mguni, David;
Wu, Yutong;
Du, Yali;
Yang, Yaodong;
Wang, Ziyi;
Li, Minne;
Wen, Ying;
... Wang, Jun; + view all
(2021)
Learning in Nonzero-Sum Stochastic Games with Potentials.
In: Meila, M and Zhang, T, (eds.)
Proceedings of Machine Learning Research (PMLR).
MLResearchPress
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Abstract
Multi-agent reinforcement learning (MARL) has become effective in tackling discrete cooperative game scenarios. However, MARL has yet to penetrate settings beyond those modelled by team and zero-sum games, confining it to a small subset of multi-agent systems. In this paper, we introduce a new generation of MARL learners that can handle nonzero-sum payoff structures and continuous settings. In particular, we study the MARL problem in a class of games known as stochastic potential games (SPGs) with continuous state-action spaces. Unlike cooperative games, in which all agents share a common reward, SPGs are capable of modelling real-world scenarios where agents seek to fulfil their individual goals. We prove theoretically our learning method, SPot-AC, enables independent agents to learn Nash equilibrium strategies in polynomial time. We demonstrate our framework tackles previously unsolvable tasks such as Coordination Navigation and large selfish routing games and that it outperforms the state of the art MARL baselines such as MADDPG and COMIX in such scenarios.
| Type: | Proceedings paper |
|---|---|
| Title: | Learning in Nonzero-Sum Stochastic Games with Potentials |
| Event: | International Conference on Machine Learning (ICML) |
| Location: | ELECTR NETWORK |
| Dates: | 18 Jul 2021 - 24 Jul 2021 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://proceedings.mlr.press/v139/ |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, GRADIENT METHODS, MULTIAGENT |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10172307 |
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