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Data-Based Efficient Off-Policy Stabilizing Optimal Control Algorithms for Discrete-Time Linear Systems via Damping Coefficients

arXiv 2412.20845 ↗First posted 30 Dec 2024eess.SY cs.SYcited by 0

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

Abstract (arXiv)

Policy iteration is one of the classical frameworks of reinforcement learning, which requires a known initial stabilizing control. However, finding the initial stabilizing control depends on the known system model. To relax this requirement and achieve model-free optimal control, in this paper, two different reinforcement learning algorithms based on policy iteration and variable damping coefficients are designed for unknown discrete-time linear systems. First, a stable artificial system is designed, and this system is gradually iterated to the original system by varying the damping coefficients. This allows the initial stabilizing control to be obtained in a finite number of iteration steps. Then, an off-policy iteration algorithm and an off-policy Q-learning algorithm are designed to select the appropriate damping coefficients and realize data-driven. In these two algorithms, the current estimates of optimal control gain are not applied to the system to re-collect data. Moreover, they are characterized by the fast convergence of the traditional policy iteration. Finally, the proposed algorithms are validated by simulation.

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 3 of its 38 references that are in the map. First posted on arXiv 30 Dec 2024.

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3 of 38 references.

References · 3

  1. 2023Deep networks for system identification: a Surveycited by 37
  2. 2021Efficient Off-Policy Q-Learning for Data-Based Discrete-Time LQR Problemscited by 9
  3. 2019Escaping Locally Optimal Decentralized Control Polices via Dampingcited by 2

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  8. 2025An Efficient Data-Driven Framework for Linear Quadratic Output Feedback Control ↗