In competitive two-agent environments, deep reinforcement learning (RL) methods based on the \emph{Double Oracle (DO)} algorithm, such as \emph{Policy Space Response Oracles (PSRO)} and \emph{Anytime PSRO (APSRO)}, iteratively add RL best response policies to a population. Eventually, an optimal mixture of these population policies will approximate a Nash equilibrium. However, these methods might need to add all deterministic policies before converging. In this work, we introduce \emph{Self-Play PSRO (SP-PSRO)}, a method that adds an approximately optimal stochastic policy to the population in each iteration. Instead of adding only deterministic best responses to the opponent's least exploitable population mixture, SP-PSRO also learns an approximately optimal stochastic policy and adds it to the population as well. As a result, SP-PSRO empirically tends to converge much faster than APSRO and in many games converges in just a few iterations.
@article{arxiv.2207.06541,
title = {Self-Play PSRO: Toward Optimal Populations in Two-Player Zero-Sum Games},
author = {Stephen McAleer and JB Lanier and Kevin Wang and Pierre Baldi and Roy Fox and Tuomas Sandholm},
journal= {arXiv preprint arXiv:2207.06541},
year = {2022}
}