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Poster
Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers
Luke Marris · Paul Muller · Marc Lanctot · Karl Tuyls · Thore Graepel

Tue Jul 20 09:00 AM -- 11:00 AM (PDT) @ Virtual
in Poster Session 1 »

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.

Keywords: Reinforcement Learning and Planning · Multi-Agent RL
[ Slides] 

Author Information

Luke Marris (DeepMind)
Paul Muller (DeepMind)
Marc Lanctot (DeepMind)
Karl Tuyls (DeepMind)
Thore Graepel (DeepMind)

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