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arXiv · 2401.05133

Neural Population Learning beyond Symmetric Zero-sum Games

Siqi Liu·Luke Marris·Marc Lanctot·Georgios Piliouras·Joel Z. Leibo·Nicolas Heess
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Abstract

We study computationally efficient methods for finding equilibria in n-player general-sum games, specifically ones that afford complex visuomotor skills. We show how existing methods would struggle in this setting, either computationally or in theory. We then introduce NeuPL-JPSRO, a neural population learning algorithm that benefits from transfer learning of skills and converges to a Coarse Correlated Equilibrium (CCE) of the game. We show empirical convergence in a suite of OpenSpiel games, validated rigorously by exact game solvers. We then deploy NeuPL-JPSRO to complex domains, where our approach enables adaptive coordination in a MuJoCo control domain and skill transfer in capture-the-flag. Our work shows that equilibrium convergent population learning can be implemented at scale and in generality, paving the way towards solving real-world games between heterogeneous players with mixed motives.

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Siqi Liu, Luke Marris, Marc Lanctot, Georgios Piliouras, Joel Z. Leibo, Nicolas Heess. 2024-01-10. Neural Population Learning beyond Symmetric Zero-sum Games. https://arxiv.org/abs/2401.05133

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