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math.OC2026
Approximations and Learning for Decentralized Stochastic Control and Near Optimal Finite Window Policies
Omar Mrani-Zentar, Serdar Yuksel
Decentralized stochastic control problems are difficult to study due to information structure dependent subtleties, which prevent many classical methods in stochastic control from…
math.OC2026
Mean-Field Systems with Heterogeneous Subteams: Optimality of Cluster-Symmetric Independent Policies and Equivalence with Decentralized McKean-Vlasov Control of Cluster-Representative Agents
Connor S. Braun, Sina Sanjari, Naci Saldi +2
Across science and engineering, mean-field methods have been a powerful and versatile approach for the analysis of systems of many interacting elements. However, common arguments u…
math.OC2026
Reinforcement Learning for Discounted and Ergodic Control of Diffusion Processes
Erhan Bayraktar, Ali D. Kara, Somnath Pradhan +1
This paper develops a quantized Q-learning algorithm for the optimal control of controlled diffusion processes on under both discounted and ergodic (average) cost cr…