mlsanityData to 14 Oct 2026

Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources

arXiv 2606.24947 ↗First posted 23 Jun 2026cs.LG cs.SY eess.SYcited by 0

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

Abstract (arXiv)

The increasing integration of distributed energy resources (DERs) is crucial for power system decarbonization, yet unlocking DERs' flexibility is challenged by their inherent uncertainties and modelling complexity. As traditional optimization methods struggle with such uncertainty and complexity of DERs, reinforcement learning (RL) has emerged as a promising alternative for DER management. However, standard RL methods suffer from sample inefficiency and sub-optimality when trained from scratch. Inspired by the training paradigms in large language models, this paper proposes a Supervised Reinforcement Learning (SRL) framework for learning DER coordination policies. This framework first pre-trains a policy on demonstration data in a supervised-learning fashion, which is then further fine-tuned using RL. Furthermore, we propose a two-step fine-tuning process: offline fine-tuning for enhancing policy performance and online fine-tuning for adapting it to the real-world dynamics. Experiments demonstrate that RL implementations based on the proposed framework significantly outperform all benchmarks, achieving high cost efficiency even under low-quality demonstration data.

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 8 of its 26 references that are in the map. First posted on arXiv 23 Jun 2026.

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8 of 26 references.

References · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
  3. 2023Understanding the Effects of RLHF on LLM Generalisation and Diversitycited by 396
  4. 2023Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuningcited by 237
  5. 2019Deep Reinforcement Learning for Smart Home Energy Managementcited by 30
  6. 2017On-line Building Energy Optimization using Deep Reinforcement Learningcited by 27
  7. 2022Scalable multi-agent reinforcement learning for distributed control of residential energy flexibilitycited by 11
  8. 2017Model Predictive Control for Distributed Microgrid Battery Energy Storage Systemscited by 4

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  1. 2025RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations
  2. 2024Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems
  3. 2026DER Allocation without Load Prediction via Reinforcement Learning
  4. 2025A Reinforcement Learning Approach for Optimal Control in Microgrids
  5. 2026Harnessing Implicit Cooperation: A Multi-Agent Reinforcement Learning Approach Towards Decentralized Local Energy Markets
  6. 2025Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges
  7. 2024Multi-Agent Reinforcement Learning for Energy Networks: Computational Challenges, Progress and Open Problems
  8. 2025Data-Driven Policy Mapping for Safe RL-based Energy Management Systems