mlsanityData to 14 Oct 2026

Lyapunov-based reinforcement learning for distributed control with stability guarantee

arXiv 2412.10844 ↗First posted 14 Dec 2024eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

In this paper, we propose a Lyapunov-based reinforcement learning method for distributed control of nonlinear systems comprising interacting subsystems with guaranteed closed-loop stability. Specifically, we conduct a detailed stability analysis and derive sufficient conditions that ensure closed-loop stability under a model-free distributed control scheme based on the Lyapunov theorem. The Lyapunov-based conditions are leveraged to guide the design of local reinforcement learning control policies for each subsystem. The local controllers only exchange scalar-valued information during the training phase, yet they do not need to communicate once the training is completed and the controllers are implemented online. The effectiveness and performance of the proposed method are evaluated using a benchmark chemical process that contains two reactors and one separator.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 52 references that are in the map. First posted on arXiv 14 Dec 2024.

Builds on

9 of 52 references.

References · 9

  1. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  2. 2018IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architecturescited by 883
  3. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 575
  4. 2018A Lyapunov-based Approach to Safe Reinforcement Learningcited by 297
  5. 2018A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximationcited by 229
  6. 2020Actor-Critic Reinforcement Learning for Control with Stability Guaranteecited by 45
  7. 2022Sample-Efficient Reinforcement Learning of Partially Observable Markov Gamescited by 27
  8. 2014On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergencecited by 22
  9. 2014Optimal Stabilization using Lyapunov Measurescited by 1

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