mlsanityData to 14 Oct 2026

On the Comparison of Reinforcement Learning and Adaptive Control for Linear Systems under Packet Loss and Uncertainty

arXiv 2606.32003 ↗First posted 30 Jun 2026eess.SY cs.SYcited by 0

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

Abstract (arXiv)

This paper presents a comparative study between Adaptive Quantized Control (AQC) and Deep Deterministic Policy Gradient (DDPG) reinforcement learning for uncertain linear systems with input quantization over communication channels subject to packet loss. The considered setting also includes dynamic switching from a nominal unstable system to a more unstable one during operation. The AQC is designed for unknown system dynamics using acknowledgment messages to compensate for packet losses, whereas the DDPG controller is trained using the nominal system model without acknowledgment messages. Numerical results show that the DDPG controller achieves faster transient responses and improved damping within its training environment. However, under model uncertainty, packet loss, and dynamic switching, the AQC consistently demonstrates superior robustness owing to its rigorous Lyapunov stability guarantees. These results highlight the trade-off between data-driven performance and model-based robustness, and provide insight into the applicability of reinforcement learning and adaptive control for networked uncertain systems.

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 10 of its 25 references that are in the map. First posted on arXiv 30 Jun 2026.

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10 of 25 references.

References · 10

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
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  6. 2019Lyapunov Criterion for Stochastic Systems and Its Applications in Distributed Computationcited by 3
  7. 2023Adaptive Covariance Kalman Filtering and Nonlinear Decoupling Control via Feedback Linearization for a Three-Tank Processcited by 1
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  9. 2024Distributed Adaptive Control of Disturbed Interconnected Systems with High-Order Tunerscited by 1
  10. 2025Model Reference Adaptive Control of Networked Systems with State and Input Delayscited by 1

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  5. 2026Actor-Identifier-Critic Reinforcement Learning for Adaptive Model-Free Optimal Control of Nonlinear Systems with Stochastic Packet Dropouts
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