mlsanityData to 14 Oct 2026

A Reinforcement Learning Engine with Reduced Action and State Space for Scalable Cyber-Physical Optimal Response

arXiv 2410.04518 ↗First posted 6 Oct 2024eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Numerous research studies have been conducted to enhance the resilience of cyber-physical systems (CPSs) by detecting potential cyber or physical disturbances. However, the development of scalable and optimal response measures under power system contingency based on fusing cyber-physical data is still in an early stage. To address this research gap, this paper introduces a power system response engine based on reinforcement learning (RL) and role and interaction discovery (RID) techniques. RL-RID-GridResponder is designed to automatically detect the contingency and assist with the decision-making process to ensure optimal power system operation. The RL-RID-GridResponder learns via an RL-based structure and achieves enhanced scalability by integrating an RID module with reduced action and state spaces. The applicability of RL-RID-GridResponder in providing scalable and optimal responses for CPSs is demonstrated on power systems in the context of Denial of Service (DoS) attacks. Moreover, simulations are conducted on a Volt-Var regulation problem using the augmented WSCC 9-bus and augmented IEEE 24-bus systems based on fused cyber and physical data sets. The results show that the proposed RL-RID-GridResponder can provide fast and accurate responses to ensure optimal power system operation under DoS and can extend to other system contingencies such as line outages and loss of loads.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 57 references that are in the map. First posted on arXiv 6 Oct 2024.

Builds on

10 of 57 references.

References · 10

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  3. 2019A Theoretical Analysis of Deep Q-Learningcited by 215
  4. 2021Reinforcement Learning for IoT Security: A Comprehensive Surveycited by 23
  5. 2021PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systemscited by 14
  6. 2021Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Trainingcited by 8
  7. 2021Multi-Source Data Fusion for Cyberattack Detection in Power Systemscited by 4
  8. 2020Considerations in the Automatic Development of Electric Grid Restoration Planscited by 1
  9. 2021Real-time Power System Simulation with Hardware Devices through DNP3 in Cyber-Physical Testbedcited by 1
  10. 2022Reinforcement Learning for Resilient Power Gridscited by 1

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  3. 2024A Review of Safe Reinforcement Learning Methods for Modern Power Systems
  4. 2024Reinforcement Learning-Based Approaches for Enhancing Security and Resilience in Smart Control: A Survey on Attack and Defense Methods
  5. 2024Implementing Deep Reinforcement Learning-Based Grid Voltage Control in Real-World Power Systems: Challenges and Insights
  6. 2024Learning-Enabled Adaptive Voltage Protection Against Load Alteration Attacks On Smart Grids ↗
  7. 2024RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors
  8. 2025Game-Theoretic Resilience Framework for Cyber-Physical Microgrids using Multi-Agent Reinforcement Learning