@inproceedings{discovery10187244,
            year = {2021},
           title = {Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers},
       publisher = {Proceedings of Machine Learning Research},
         journal = {Proceedings of the 38th International Conference on Machine Learning},
       booktitle = {Proceedings of the 38th International Conference on Machine Learning},
           pages = {7480--7491},
          editor = {M Meila and T Zhang},
          volume = {139},
            note = {{\copyright} The Authors 2023. Original content in this paper is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/).},
          author = {Marris, Luke and Muller, Paul and Lanctot, Marc and Tuyls, Karl and Graepel, Thore},
        abstract = {Two-player, constant-sum games are well studied in the literature, but there has been limited progress outside of this setting. We propose Joint Policy-Space Response Oracles (JPSRO), an algorithm for training agents in n-player, general-sum extensive form games, which provably converges to an equilibrium. We further suggest correlated equilibria (CE) as promising meta-solvers, and propose a novel solution concept Maximum Gini Correlated Equilibrium (MGCE), a principled and computationally efficient family of solutions for solving the correlated equilibrium selection problem. We conduct several experiments using CE meta-solvers for JPSRO and demonstrate convergence on n-player, general-sum games.},
             url = {https://proceedings.mlr.press/v139/marris21a.html}
}