# Cooperative Energy Scheduling of Multi-Microgrids Based on Risk-Sensitive Reinforcement Learning

arXiv: https://arxiv.org/abs/2512.17246 · HTML: https://mlsanity.com/paper/2512.17246/

## 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 7 of its 27 references that are in the map.
- First posted on arXiv 19 Dec 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 7 of 7)

- [Value-Decomposition Multi-Agent Actor-Critics](https://mlsanity.com/paper/2007.12306/) (2020)
- [SCRIMP: Scalable Communication for Reinforcement- and Imitation-Learning-Based Multi-Agent Pathfinding](https://mlsanity.com/paper/2303.00605/) (2023)
- [Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learning](https://mlsanity.com/paper/2310.01207/) (2023)
- [RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization](https://mlsanity.com/paper/2311.01753/) (2023)
- [CaEGCN: Cross-Attention Fusion based Enhanced Graph Convolutional Network for Clustering](https://mlsanity.com/paper/2101.06883/) (2021)
- [Optimal Energy System Scheduling Using A Constraint-Aware Reinforcement Learning Algorithm](https://mlsanity.com/paper/2305.05484/) (2023)
- [SpaceRIS: LEO Satellite Coverage Maximization in 6G Sub-THz Networks by MAPPO DRL and Whale Optimization](https://mlsanity.com/paper/2307.15469/) (2023)

## Abstract (arXiv)

With the rapid development of distributed renewable energy, multi-microgrids play an increasingly important role in improving the flexibility and reliability of energy supply. Reinforcement learning has shown great potential in coordination strategies due to its model-free nature. Current methods lack explicit quantification of the relationship between individual and joint risk values, resulting in obscured credit assignment. Moreover, they often depend on explicit communication, which becomes inefficient as system complexity grows. To address these challenges, this paper proposes a risk-sensitive reinforcement learning framework with shared memory (RRL-SM) for multi-microgrid scheduling. Specifically, a risk-sensitive value factorization scheme is proposed to quantify the relationship between individual and joint risk values by leveraging distributional modeling and attention-based representations, thereby aligning local decisions with global risk objectives. An implicit shared-memory coordination mechanism is implemented through a global memory space to enhance the overall efficiency of decentralized decision-making. Collectively, the integrated approach delivers more reliable cooperative scheduling under renewable energy uncertainty. Simulation results show that RRL-SM reduces load-shedding risk by 84.5%, demonstrating a favorable balance between reliability and economic performance.