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[2602.16063] MARLEM: A Multi-Agent Reinforcement Learning Simulation Framework for Implicit Cooperation in Decentralized Local Energy Markets
Summary
The paper presents MARLEM, a novel multi-agent reinforcement learning framework designed for studying implicit cooperation in decentralized local energy markets, enhancing system performance through agent collaboration.
Why It Matters
As energy markets transition towards decentralization, understanding how agents can implicitly cooperate is crucial for improving efficiency and stability. MARLEM provides a flexible tool for researchers to explore these dynamics, potentially influencing future energy systems.
Key Takeaways
- MARLEM is an open-source framework for multi-agent reinforcement learning in energy markets.
- The framework enables agents to learn strategies that promote system-wide benefits without explicit communication.
- It includes a modular market platform and analytics suite for evaluating agent performance.
- Case studies demonstrate how different market configurations affect overall system performance.
- MARLEM can facilitate emergent coordination and improve grid stability.
Electrical Engineering and Systems Science > Systems and Control arXiv:2602.16063 (eess) [Submitted on 17 Feb 2026] Title:MARLEM: A Multi-Agent Reinforcement Learning Simulation Framework for Implicit Cooperation in Decentralized Local Energy Markets Authors:Nelson Salazar-Pena, Alejandra Tabares, Andres Gonzalez-Mancera View a PDF of the paper titled MARLEM: A Multi-Agent Reinforcement Learning Simulation Framework for Implicit Cooperation in Decentralized Local Energy Markets, by Nelson Salazar-Pena and 2 other authors View PDF HTML (experimental) Abstract:This paper introduces a novel, open-source MARL simulation framework for studying implicit cooperation in LEMs, modeled as a decentralized partially observable Markov decision process and implemented as a Gymnasium environment for MARL. Our framework features a modular market platform with plug-and-play clearing mechanisms, physically constrained agent models (including battery storage), a realistic grid network, and a comprehensive analytics suite to evaluate emergent coordination. The main contribution is a novel method to foster implicit cooperation, where agents' observations and rewards are enhanced with system-level key performance indicators to enable them to independently learn strategies that benefit the entire system and aim for collectively beneficial outcomes without explicit communication. Through representative case studies (available in a dedicated GitHub repository in this https URL, we show the framewo...
