Multi-Agent Training beyond Zero-Sum with Correlated Equilibrium Meta-Solvers

Outside of normal form (NF) games, this problem setting Two-player, constant-sum games are well studied arises in multi-agent training when dealing with empirical in the literature, but there has been limited games (also called meta-games), where a game payoff progress outside of this setting. We propose Joint tensor is populated with expected outcomes between Policy-Space Response Oracles (JPSRO), an algorithm agents playing an extensive form (EF) game, for example for training agents in n-player, general-sum the StarCraft League (Vinyals et al., 2019) and Policy-Space extensive form games, which provably converges Response Oracles (PSRO) (Lanctot et al., 2017), a recent to an equilibrium. We further suggest correlated variant of which reached state-of-the-art results in Stratego equilibria (CE) as promising meta-solvers, and Barrage (McAleer et al., 2020).