Partially Observed Optimal Stochastic Control: Regularity, Optimality, Approximations, and Learning
AD Kara, S Yuksel - arxiv preprint arxiv:2412.06735, 2024 - arxiv.org
In this review/tutorial article, we present recent progress on optimal control of partially
observed Markov Decision Processes (POMDPs). We first present regularity and continuity …
observed Markov Decision Processes (POMDPs). We first present regularity and continuity …
A Theoretical Justification for Asymmetric Actor-Critic Algorithms
In reinforcement learning for partially observable environments, many successful algorithms
were developed within the asymmetric learning paradigm. This paradigm leverages …
were developed within the asymmetric learning paradigm. This paradigm leverages …
Refined Bounds on Near Optimality Finite Window Policies in POMDPs and Their Reinforcement Learning
Finding optimal policies for Partially Observable Markov Decision Processes (POMDPs) is
challenging due to their uncountable state spaces when transformed into fully observable …
challenging due to their uncountable state spaces when transformed into fully observable …