Internal State-Based Policy Gradient Methods for Partially Observable Markov Potential Games
This letter studies multi-agent reinforcement learning in partially observable Markov potential games. Solving this problem is challenging due to partial observability, decentralized information, and the curse of dimensionality. First, to address the first two challenges, we leverage the common information framework, which allows agents to act based on both shared and local information. Second, to ensure tractability, we study an internal state that compresses accumulated information, preventing it from growing unboundedly over time. We then implement an internal state-based natural policy gradient method to find Nash equilibria of the Markov potential game. Our main contribution is to es...
| Authors | Wonseok Yang, Thinh T. Doan |
|---|---|
| Primary category | cs.MA |
| Published | 1 April 2026 |
| Last updated at source | 1 April 2026 |
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