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cs.GT2026
Independent Learning of Nash Equilibria in Partially Observable Markov Potential Games with Decoupled Dynamics
Philip Jordan, Maryam Kamgarpour
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…
cs.GT2025
Nash Equilibria in Games with Playerwise Concave Coupling Constraints: Existence and Computation
Philip Jordan, Maryam Kamgarpour
We study the existence and computation of Nash equilibria in concave games where the players' admissible strategies are subject to shared coupling constraints. Under playerwise con…