Revisiting Regularized Policy Optimization for Stable and Efficient Reinforcement Learning in Two-Player Games

arXiv:2602.10894v2 Announce Type: replace Abstract: Two-player games such as board games have long been used as traditional benchmarks for reinforcement learning. This work revisits a policy optimization method with reverse Kullback-Leibler regularization and entropy regularization and analyzes this combination in two-player zero-sum settings from theoretical and empirical perspectives. From a theoretical perspective, we investigate the stability of the policy update rule in two theoretical settings: game-theoretic normal-form games and finite-length games. We provide novel convergence guarant

Source: arXiv cs.LG — read the full report at the original publisher.

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