mlsanityData to 14 Oct 2026

LLM-Enhanced Multi-Agent Reinforcement Learning with Expert Workflow for Real-Time P2P Energy Trading

arXiv 2507.14995 ↗First posted 20 Jul 2025cs.MAcited by 2

On the map: LLM agents and tool use → Financial trading and forecasting agents

Abstract (arXiv)

Real-time peer-to-peer (P2P) electricity markets dynamically adapt to fluctuations in renewable energy and variations in demand, maximizing economic benefits through instantaneous price responses while enhancing grid flexibility. However, scaling expert guidance for massive personalized prosumers poses critical challenges, including diverse decision-making demands and a lack of customized modeling frameworks. This paper proposes an integrated large language model-multi-agent reinforcement learning (LLM-MARL) framework for real-time P2P energy trading to address challenges such as the limited technical capability of prosumers, the lack of expert experience, and security issues of distribution networks. LLMs are introduced as experts to generate personalized strategies, guiding MARL under the centralized training with decentralized execution (CTDE) paradigm through imitation. To handle the scalability issues inherent in large-scale P2P networks, a differential attention-based critic network is introduced to efficiently extract key interaction features and enhance convergence. Experimental results demonstrate that LLM-generated strategies effectively substitute human experts. The proposed imitative expert MARL algorithms achieve significantly lower economic costs and voltage violation rates on test sets compared to baseline algorithms, while maintaining robust stability. This paper provides an effective solution for the real-time decision-making of the P2P electricity market by bridging expert knowledge with agent learning.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. Builds on 24 of its 68 references that are in the map. First posted on arXiv 20 Jul 2025.

Builds on

24 of 68 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework21 Nov

Other references · 23

  1. 2021A Minimalist Approach to Offline Reinforcement Learningcited by 899
  2. 2018Computational Optimal Transportcited by 780
  3. 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 641
  4. 2018Quantifying Generalization in Reinforcement Learningcited by 352
  5. 2018Actor-Attention-Critic for Multi-Agent Reinforcement Learningcited by 261
  6. 2024ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modelingcited by 124
  7. 2018Multi-Agent Generative Adversarial Imitation Learningcited by 100
  8. 2017A Survey of Learning in Multiagent Environments: Dealing with Non-Stationaritycited by 94
  9. 2021Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Drivingcited by 47
  10. 2019Making Efficient Use of Demonstrations to Solve Hard Exploration Problemscited by 45
  11. 2023BenchMARL: Benchmarking Multi-Agent Reinforcement Learningcited by 33
  12. 2023Is Centralized Training with Decentralized Execution Framework Centralized Enough for MARL?cited by 18
11 more
  1. 2023Large Foundation Models for Power Systemscited by 18
  2. 2025TeLL-Drive: Enhancing Autonomous Driving with Teacher LLM-Guided Deep Reinforcement Learningcited by 13
  3. 2024A Review of Safe Reinforcement Learning Methods for Modern Power Systemscited by 11
  4. 2022A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learningcited by 10
  5. 2024RL2: Reinforce Large Language Model to Assist Safe Reinforcement Learning for Energy Management of Active Distribution Networkscited by 9
  6. 2024Large Language Model guided Deep Reinforcement Learning for Decision Making in Autonomous Drivingcited by 8
  7. 2025Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generationcited by 4
  8. 2020Distributed Energy Trading and Scheduling among Microgrids via Multiagent Reinforcement Learningcited by 3
  9. 2020Hybrid Imitation Learning for Real-Time Service Restoration in Resilient Distribution Systemscited by 1
  10. 2024Peer-to-Peer Energy Trading of Solar and Energy Storage: A Networked Multiagent Reinforcement Learning Approachcited by 1
  11. 2025Online distributed optimization for spatio-temporally constrained real-time peer-to-peer energy tradingcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026A MARL Centered Reference Architecture for Large Language Model Augmentation in Smart Manufacturingcited by 0
  2. 2026Grounded, Compute-Efficient LLM Policy Agents for Energy-Poverty Equity in Physically-Constrained Peer-to-Peer Energy Marketscited by 0

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  8. 2024Reinforcement Learning Enabled Peer-to-Peer Energy Trading for Dairy Farms