A Generalization of the Borkar-Meyn Theorem for Stochastic Recursive Inclusions
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Abstract (arXiv)
In this paper the stability theorem of Borkar and Meyn is extended to include the case when the mean field is a differential inclusion. Two different sets of sufficient conditions are presented that guarantee the stability and convergence of stochastic recursive inclusions. Our work builds on the works of Benaim, Hofbauer and Sorin as well as Borkar and Meyn. As a corollary to one of the main theorems, a natural generalization of the Borkar and Meyn Theorem follows. In addition, the original theorem of Borkar and Meyn is shown to hold under slightly relaxed assumptions. Finally, as an application to one of the main theorems we discuss a solution to the approximate drift problem.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Feb 2015.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 3; 2024: 0; 2025: 2.
7 papers.
- 2021The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learningcited by 29
- 2023Stability of Q-Learning Through Design and Optimismcited by 7
- 2023n-Step Temporal Difference Learning with Optimal ncited by 2
- 2021Fully Distributed Actor-Critic Architecture for Multitask Deep Reinforcement Learningcited by 1
- 2025The late-stage training dynamics of (stochastic) subgradient descent on homogeneous neural networkscited by 1
- 2023Stability and Convergence of Distributed Stochastic Approximations with large Unbounded Stochastic Information Delayscited by 0
- 2025Convergent Reinforcement Learning Algorithms for Stochastic Shortest Path Problemcited by 0
- 2025Convergent Reinforcement Learning Algorithms for Stochastic Shortest Path Problem19 Aug 2025
- 2025The late-stage training dynamics of (stochastic) subgradient descent on homogeneous neural networks8 Feb 2025
- 2023Stability of Q-Learning Through Design and Optimism5 Jul 2023
- 2023Stability and Convergence of Distributed Stochastic Approximations with large Unbounded Stochastic Information Delays11 May 2023
- 2023n-Step Temporal Difference Learning with Optimal n13 Mar 2023
- 2021The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning27 Oct 2021
- 2021Fully Distributed Actor-Critic Architecture for Multitask Deep Reinforcement Learning23 Oct 2021