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We ran 5,600 hyperparameter sweeps to compare RL algorithms on hidden-information games with billions of states.
In our benchmark, we found that properly tuned policy gradient methods, such as PPO, performed the best.
Paper: arxiv.org/abs/2502.08938
In our benchmark, we found that properly tuned policy gradient methods, such as PPO, performed the best.
Paper: arxiv.org/abs/2502.08938
arxiv.org Reevaluating Policy Gradient Methods for Imperfect-Information Games 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... Model-free deep RL algorithms like NFSP, PSRO, ESCHER, & R-NaD are tailor-made for games with hidden information (e.g. poker).
We performed the largest-ever comparison of these algorithms.
We find that they do not outperform generic policy gradient methods, such as PPO. 1/NView quoted post on X
