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cs.LG2026
Reinforcement Learning with Function Approximation for Non-Markov Processes
Ali Devran Kara
We study reinforcement learning methods with linear function approximation under non-Markov state and cost processes. We first consider the policy evaluation method and show that t…
cs.LG2021★ 3 cited
Q-Learning for MDPs with General Spaces: Convergence and Near Optimality via Quantization under Weak Continuity
Ali Devran Kara, Naci Saldi, Serdar Yüksel
Reinforcement learning algorithms often require finiteness of state and action spaces in Markov decision processes (MDPs) (also called controlled Markov chains) and various efforts…
cs.LG2021
Convergence of Finite Memory Q-Learning for POMDPs and Near Optimality of Learned Policies under Filter Stability
Ali Devran Kara, Serdar Yuksel
In this paper, for POMDPs, we provide the convergence of a Q learning algorithm for control policies using a finite history of past observations and control actions, and, consequen…