Reevaluating Policy Gradient Methods for Imperfect-Information Games
Abstract
In the past decade, motivated by the putative failure of naive self-play deep reinforcement learning (DRL) in adversarial imperfect-information games, researchers have developed numerous DRL algorithms based on fictitious play (FP), double oracle (DO), and counterfactual regret minimization (CFR). In light of recent results of the magnetic mirror descent algorithm, we hypothesize that simpler generic policy gradient methods like PPO are competitive with or superior to these FP-, DO-, and CFR-based DRL approaches. To facilitate the resolution of this hypothesis, we implement and release the first broadly accessible exact exploitability computations for five large games. Using these games, we conduct the largest-ever exploitability comparison of DRL algorithms for imperfect-information games. Over 7000 training runs, we find that FP-, DO-, and CFR-based approaches fail to outperform generic policy gradient methods. Code is available at https://github.com/nathanlct/IIG-RL-Benchmark and https://github.com/gabrfarina/exp-a-spiel .
Cite
@article{arxiv.2502.08938,
title = {Reevaluating Policy Gradient Methods for Imperfect-Information Games},
author = {Max Rudolph and Nathan Lichtle and Sobhan Mohammadpour and Alexandre Bayen and J. Zico Kolter and Amy Zhang and Gabriele Farina and Eugene Vinitsky and Samuel Sokota},
journal= {arXiv preprint arXiv:2502.08938},
year = {2026}
}
Comments
International Conference on Learning Representations (ICLR) 2026