mlsanityData to 14 Oct 2026

Model-Free Reinforcement Learning Control for Resilient Cyber-Physical Systems

arXiv 2606.19069 ↗First posted 17 Jun 2026eess.SY cs.LG cs.SYcited by 0

On the map: Deep reinforcement learning methods → Safe and constrained RL

Abstract (arXiv)

This paper compares the performance of model-free controllers on a nonlinear system under cyberattacks, including false data injection and denial-of-service attacks. Four RL reward types are analyzed for accuracy, cost, and resilience. Results show that the Lyapunov reward offers the best resilience with low tracking error. Exponential mode also provides good trade-offs with acceptable resilience under moderate training conditions. Progressive and linear rewards converge faster but are less robust. RL-MPCs show strong steady-state resilience but require longer training times; RL-PID controllers are faster with significantly less training time. Proximal Policy Optimization outperforms Deep Deterministic Policy Gradient with a significant reduction in KPI variance. This study serves to highlight how well-designed RL rewards can improve performance and resilience against cyber threats.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 19 references that are in the map. First posted on arXiv 17 Jun 2026.

Builds on

4 of 19 references.

References · 4

  1. 2024Reinforcement learningcited by 247
  2. 2024Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applicationscited by 38
  3. 2022Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approachcited by 12
  4. 2024Machine learning for industrial sensing and control: A survey and practical perspectivecited by 8

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  1. 2025Evaluating Reinforcement Learning Safety and Trustworthiness in Cyber-Physical Systems
  2. 2025ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning
  3. 2026Beyond Rewards in Reinforcement Learning for Cyber Defence
  4. 2026Safe Reinforcement Learning using Ideas from Model Predictive Control
  5. 2025Less is more? Rewards in RL for Cyber Defence
  6. 2025Quantitative Resilience Modeling for Autonomous Cyber Defense
  7. 2025Guidelines for Applying RL and MARL in Cybersecurity Applications
  8. 2024A Reinforcement Learning Engine with Reduced Action and State Space for Scalable Cyber-Physical Optimal Response