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[2602.16062] Harnessing Implicit Cooperation: A Multi-Agent Reinforcement Learning Approach Towards Decentralized Local Energy Markets
Summary
This paper presents a framework for decentralized local energy markets using implicit cooperation among agents, optimizing coordination without direct communication.
Why It Matters
As energy markets evolve towards decentralization, this research offers a novel approach to enhance efficiency and stability in energy distribution, critical for sustainable energy management. The findings could influence future designs of energy systems, promoting privacy and reducing reliance on centralized infrastructures.
Key Takeaways
- Implicit cooperation allows decentralized agents to coordinate effectively without direct communication.
- The APPO-DTDE configuration achieved a 91.7% coordination score compared to centralized benchmarks.
- Decentralized approaches demonstrate improved stability and reduced grid balance variance by 31%.
Electrical Engineering and Systems Science > Systems and Control arXiv:2602.16062 (eess) [Submitted on 17 Feb 2026] Title:Harnessing Implicit Cooperation: A Multi-Agent Reinforcement Learning Approach Towards Decentralized Local Energy Markets Authors:Nelson Salazar-Pena, Alejandra Tabares, Andres Gonzalez-Mancera View a PDF of the paper titled Harnessing Implicit Cooperation: A Multi-Agent Reinforcement Learning Approach Towards Decentralized Local Energy Markets, by Nelson Salazar-Pena and 2 other authors View PDF HTML (experimental) Abstract:This paper proposes implicit cooperation, a framework enabling decentralized agents to approximate optimal coordination in local energy markets without explicit peer-to-peer communication. We formulate the problem as a decentralized partially observable Markov decision problem that is solved through a multi-agent reinforcement learning task in which agents use stigmergic signals (key performance indicators at the system level) to infer and react to global states. Through a 3x3 factorial design on an IEEE 34-node topology, we evaluated three training paradigms (CTCE, CTDE, DTDE) and three algorithms (PPO, APPO, SAC). Results identify APPO-DTDE as the optimal configuration, achieving a coordination score of 91.7% relative to the theoretical centralized benchmark (CTCE). However, a critical trade-off emerges between efficiency and stability: while the centralized benchmark maximizes allocative efficiency with a peer-to-peer trade ratio...
