arXiv:2606.23995v1 Announce Type: cross Abstract: Recent work has established that regularized policy gradient methods such as PPO, when used in self-play, can match or exceed specialized game-theoretic algorithms for solving two-player zero-sum imperfect-information games. The uniform distribution has emerged as a strong policy regularization target for this purpose, but it regularizes equally toward all actions regardless of their viability. We introduce EMAgnet, which instead regularizes toward an exponential moving average (EMA) of the last-iterate policy's parameters, providing an adaptiv
The continuous advancements in AI research necessitate improved training methods for complex game-theoretic scenarios, with regularized policy gradients showing increasing promise.
This research introduces a novel, more adaptive regularization technique that could significantly enhance the efficiency and performance of AI in competitive, imperfect-information environments, impacting areas like autonomous agents and strategic decision-making.
The method of regularizing policy gradients in self-play is refined from a uniform approach to an adaptive, parameter-space exponential moving average, potentially leading to more robust and effective AI policies.
- · AI researchers
- · Game AI developers
- · Reinforcement learning platforms
- · Companies developing autonomous agents
- · Less adaptive policy gradient methods
- · Developers solely relying on uniform regularization
Improved performance of AI agents in complex multi-player games and simulations.
Faster development and deployment of sophisticated AI for strategic applications beyond gaming.
Potential for AI systems to learn optimal strategies in real-world scenarios with incomplete information, accelerating automation and decision support across industries.
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Read at arXiv cs.AI