Weak Feller Property of Non-linear Filters
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Abstract (arXiv)
Weak Feller property of controlled and control-free Markov chains lead to many desirable properties. In control-free setups this leads to the existence of invariant probability measures for compact spaces and applicability of numerical approximation methods. For controlled setups, this leads to existence and approximation results for optimal control policies. We know from stochastic control theory that partially observed systems can be converted to fully observed systems by replacing the original state space with a probability measure-valued state space, with the corresponding kernel acting on probability measures known as the non-linear filter (belief) process. Establishing sufficient conditions for the weak Feller property for such processes is a significant problem, studied under various assumptions and setups in the literature. In this paper, we prove the weak Feller property of the non-linear filter process (i) first under weak continuity of the transition probability of controlled Markov chain and total variation continuity of its observation channel, and then, (ii) under total variation continuity of the transition probability of controlled Markov chain. The former result (i) has first appeared in Feinberg et. al. [Math. Oper. Res. 41(2) (2016) 656-681]. Here, we present a concise and easy to follow alternative proof for this existing result. The latter result (ii) establishes weak Feller property of non-linear filter process under conditions, which have not been previously reported in the literature.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Dec 2018.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 1; 2025: 1; 2026: 1.
6 papers.
- 2021Convergence of Finite Memory Q-Learning for POMDPs and Near Optimality of Learned Policies under Filter Stabilitycited by 22
- 2021Q-Learning for MDPs with General Spaces: Convergence and Near Optimality via Quantization under Weak Continuitycited by 11
- 2023Q-Learning for Stochastic Control under General Information Structures and Non-Markovian Environmentscited by 7
- 2024Refined Bounds on Near Optimality Finite Window Policies in POMDPs and Their Reinforcement Learningcited by 0
- 2025Learning POMDPs with Linear Function Approximation and Finite Memorycited by 0
- 2026Reinforcement Learning with Function Approximation for Non-Markov Processescited by 0
- 2026Reinforcement Learning with Function Approximation for Non-Markov Processes1 Jan 2026
- 2025Learning POMDPs with Linear Function Approximation and Finite Memory20 May 2025
- 2024Refined Bounds on Near Optimality Finite Window Policies in POMDPs and Their Reinforcement Learning6 Sep 2024
- 2023Q-Learning for Stochastic Control under General Information Structures and Non-Markovian Environments31 Oct 2023
- 2021Q-Learning for MDPs with General Spaces: Convergence and Near Optimality via Quantization under Weak Continuity12 Nov 2021
- 2021Convergence of Finite Memory Q-Learning for POMDPs and Near Optimality of Learned Policies under Filter Stability22 Mar 2021