17 papers
Near Optimality of Discrete-Time Approximations for Controlled McKean-Vlasov and Large Interacting Particle Diffusions
Somnath Pradhan, Serdar Yuksel
We study stochastic optimal control problems for (possibly degenerate) McKean-Vlasov controlled diffusions and obtain discrete-time as well as finite interacting particle approxima…
Approximations and Learning for Continuous State and Action MDPs under Average Cost Criteria
Ali Devran Kara, Serdar Yuksel
In this paper, for Markov Decision Processes (MDPs) with standard Borel spaces, (i) we first provide a discretization based approximation method for MDPs with continuous spaces und…
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…
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…
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…
Centralized Reduction of Decentralized Stochastic Control Models and their weak-Feller Regularity
Omar Mrani-Zentar, Serdar Yüksel
Decentralized stochastic control problems involving general state/measurement/action spaces are intrinsically difficult to study because of the inapplicability of standard tools fr…