13 papers · 1 filter
Mean-Field Control with a Common Hidden State under Decentralized Observations
Erhan Bayraktar, Ali D. Kara
We study optimal control of a system with multiple decision makers who share a common hidden state and receive fully decentralized observations through identical channels. The dyna…
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…
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…
Quantizer Design for Finite Model Approximations, Model Learning, and Quantized Q-Learning for MDPs with Unbounded Spaces
Osman Bicer, Ali D. Kara, Serdar Yuksel
In this paper, for Markov decision processes (MDPs) with unbounded state spaces we present refined upper bounds presented in [Kara et. al. JMLR'23] on finite model approximation er…
Sensitivity of Filter Kernels and Robustness Bounds to Transition and Measurement Kernel Perturbations in Partially Observable Stochastic Control
Yunus Emre Demirci, Ali Devran Kara, Serdar Yüksel
Studying the stability of partially observed Markov decision processes (POMDPs) with respect to perturbations in either transition or observation kernels is a significant problem.…
Learning with Linear Function Approximations in Mean-Field Control
Erhan Bayraktar, Ali D. Kara
The paper focuses on mean-field type multi-agent control problems with finite state and action spaces where the dynamics and cost structures are symmetric and homogeneous, and are…