mlsanityData to 14 Oct 2026

Shielded Controller Units for RL with Operational Constraints Applied to Remote Microgrids

arXiv 2512.01046 ↗First posted 30 Nov 2025cs.AI cs.LGcited by 0

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

Abstract (arXiv)

Reinforcement learning (RL) is a powerful framework for optimizing decision-making in complex systems under uncertainty, an essential challenge in real-world settings, particularly in the context of the energy transition. A representative example is remote microgrids that supply power to communities disconnected from the main grid. Enabling the energy transition in such systems requires coordinated control of renewable sources like wind turbines, alongside fuel generators and batteries, to meet demand while minimizing fuel consumption and battery degradation under exogenous and intermittent load and wind conditions. These systems must often conform to extensive regulations and complex operational constraints. To ensure that RL agents respect these constraints, it is crucial to provide interpretable guarantees. In this paper, we introduce Shielded Controller Units (SCUs), a systematic and interpretable approach that leverages prior knowledge of system dynamics to ensure constraint satisfaction. Our shield synthesis methodology, designed for real-world deployment, decomposes the environment into a hierarchical structure where each SCU explicitly manages a subset of constraints. We demonstrate the effectiveness of SCUs on a remote microgrid optimization task with strict operational requirements. The RL agent, equipped with SCUs, achieves a 24% reduction in fuel consumption without increasing battery degradation, outperforming other baselines while satisfying all constraints. We hope SCUs contribute to the safe application of RL to the many decision-making challenges linked to the energy transition.

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 20 of its 45 references that are in the map. First posted on arXiv 30 Nov 2025.

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20 of 45 references.

References · 20

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2016Concrete Problems in AI Safetycited by 1,642
  4. 2017Constrained Policy Optimizationcited by 983
  5. 2017Safe Reinforcement Learning via Shieldingcited by 449
  6. 2019Challenges of Real-World Reinforcement Learningcited by 291
  7. 2023The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systemscited by 88
  8. 2022Constrained Variational Policy Optimization for Safe Reinforcement Learningcited by 71
  9. 2022Safe Reinforcement Learning via Shielding under Partial Observabilitycited by 56
  10. 2024A Survey of Constraint Formulations in Safe Reinforcement Learningcited by 44
  11. 2022Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarkingcited by 34
  12. 2019Reinforcement Learning with Probabilistic Guarantees for Autonomous Drivingcited by 28
8 more
  1. 2024Dynamic Model Predictive Shielding for Provably Safe Reinforcement Learningcited by 15
  2. 2022Dynamic Shielding for Reinforcement Learning in Black-Box Environmentscited by 11
  3. 2022Contingency-constrained economic dispatch with safe reinforcement learningcited by 8
  4. 2022Safe reinforcement learning for multi-energy management systems with known constraint functionscited by 7
  5. 2021Reinforcement Learning Based Optimal Battery Control Under Cycle-based Degradation Costcited by 6
  6. 2021SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulationcited by 5
  7. 2020It's Time to Play Safe: Shield Synthesis for Timed Systemscited by 4
  8. 2018Emergence and Evolution of Hierarchical Structure in Complex Systemscited by 2

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  3. 2024Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents
  4. 2025A Reinforcement Learning Approach for Optimal Control in Microgrids
  5. 2024Learning a local trading strategy: deep reinforcement learning for grid-scale renewable energy integration
  6. 2026Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources
  7. 2025Towards Efficient Multi-Objective Optimisation for Real-World Power Grid Topology Control
  8. 2024Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems