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
The continuous research in AI, particularly reinforcement learning, is driven by the quest for more robust and efficient algorithms applicable to complex decision-making scenarios.
Improved stability and efficiency in reinforcement learning, especially in game theory settings, is crucial for advancing autonomous AI systems and agents where stable performance is paramount.
This research provides theoretical and empirical insights into a regularized policy optimization method, potentially leading to more reliable AI training paradigms.
- · AI researchers
- · Game AI developers
- · Reinforcement learning platforms
- · Inefficient RL algorithms
- · AI systems prone to instability
More stable and predictable AI agents can be developed for various applications.
This could accelerate the deployment of autonomous AI in more sensitive and competitive environments.
Enhanced AI stability may reduce development cycles and increase the overall trustworthiness of AI systems.
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Read at arXiv cs.LG