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Harnessing Implicit Cooperation: A Multi-Agent Reinforcement Learning Approach Towards Decentralized Local Energy Markets

arXiv 2602.16062 ↗First posted 17 Feb 2026eess.SY cs.CE cs.LGcited by 0

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Abstract (arXiv)

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 of 0.6, the fully decentralized approach (DTDE) demonstrates superior physical stability. Specifically, DTDE reduces the variance of grid balance by 31% compared to hybrid architectures, establishing a highly predictable, import-biased load profile that simplifies grid regulation. Furthermore, topological analysis reveals emergent spatial clustering, where decentralized agents self-organize into stable trading communities to minimize congestion penalties. While SAC excelled in hybrid settings, it failed in decentralized environments due to entropy-driven instability. This research proves that stigmergic signaling provides sufficient context for complex grid coordination, offering a robust, privacy-preserving alternative to expensive centralized communication infrastructure.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 119 references that are in the map. First posted on arXiv 17 Feb 2026.

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28 of 119 references.

References · 28

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16 more
  1. 2022Energy Management Based on Multi-Agent Deep Reinforcement Learning for A Multi-Energy Industrial Parkcited by 5
  2. 2022Proximal Policy Optimization Based Reinforcement Learning for Joint Bidding in Energy and Frequency Regulation Marketscited by 5
  3. 2023Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibilitycited by 5
  4. 2024An Initial Introduction to Cooperative Multi-Agent Reinforcement Learningcited by 5
  5. 2025Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Controlcited by 5
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  10. 2025An Efficient Open World Environment for Multi-Agent Social Learningcited by 3
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  12. 2023Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithmcited by 2
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  14. 2025A Generative Model Enhanced Multi-Agent Reinforcement Learning Method for Electric Vehicle Charging Navigationcited by 1
  15. 2025Safe Bottom-Up Flexibility Provision from Distributed Energy Resourcescited by 1
  16. 2025AutoGrid AI: Deep Reinforcement Learning Framework for Autonomous Microgrid Managementcited by 1

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