AI RESEARCH
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics
arXiv CS.LG
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ArXi:2605.06377v1 Announce Type: cross We study Nash equilibrium learning in partially observable Marko 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.