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 computati