6 citations · 15 across the 20 of their papers we have counts for
4 papers · 2 filters
Average Cost Optimality of Partially Observed MDPS: Contraction of Non-linear Filters, Optimal Solutions and Approximations
Yunus Emre Demirci, Ali Devran Kara, Serdar Yüksel
The average cost optimality is known to be a challenging problem for partially observable stochastic control, with few results available beyond the finite state, action, and measur…
Q-Learning for Stochastic Control under General Information Structures and Non-Markovian Environments
Ali Devran Kara, Serdar Yuksel
As a primary contribution, we present a convergence theorem for stochastic iterations, and in particular, Q-learning iterates, under a general, possibly non-Markovian, stochastic e…
Infinite Horizon Average Cost Optimality Criteria for Mean-Field Control
Erhan Bayraktar, Ali D. Kara
We study mean-field control problems in discrete-time under the infinite horizon average cost optimality criteria. We focus on both the finite population and the infinite populatio…
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…