Pinned
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
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/N
