Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics
We study Nash equilibrium learning in partially observable Markov games (POMGs), a multi-agent reinforcement learning framework in which agents cannot fully observe the underlying state. Prior work in this setting relies on centralization or information sharing, and suffers from sample and computational complexity that scales exponentially in the number of players. We focus on a subclass of POMGs with independent state transitions, where agents remain coupled through their rewards, and assume that the underlying fully observed Markov game is a Markov potential game. For this class, we present an independent learning algorithm in which players, observing only their own actions and observat...
| Authors | Philip Jordan, Maryam Kamgarpour |
|---|---|
| Primary category | cs.GT |
| Published | 7 May 2026 |
| Last updated at source | 11 September 2026 |
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