Scalable Multi-Agent Reinforcement Learning for Decentralized Energy Grid Optimization

This paper presents a novel multi-agent reinforcement learning framework that significantly improves the efficiency and stability of decentralized energy grid management by optimizing renewable energy integration and demand response in real-time.