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News/Revisiting Regularized Policy Optimization for Stable and Efficient Reinforcement Learning in Two-Player Games
arxiv
PublishedMay 22, 2026 at 4:00 AM
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Revisiting Regularized Policy Optimization for Stable and Efficient Reinforcement Learning in Two-Player Games

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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

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