mlsanityData to 14 Oct 2026

LLM-Enhanced Multi-Agent Reinforcement Learning for Unified Electric Vehicles-Charging Station-Grid Optimization in Public Charging Systems

arXiv 2609.13805 ↗First posted 12 Sep 2026cs.AIcited by 0

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

Abstract (arXiv)

In the era of the Internet of Things (IoT), coordinating connected electric vehicle (EV) charging scheduling to balance EV charging satisfaction, station profitability, and smart grid stability presents a complex multi-objective challenge. Existing Multi-Agent Reinforcement Learning (MARL) approaches often struggle with high-dimensional state spaces generated by massive IoT sensing data and conflicting stakeholder interests. This paper proposes a novel LLM-enhanced MARL framework that, for the first time, simultaneously optimizes the Grid, EVs, and Stations within a unified loop. By integrating Large Language Model (LLM), we address two critical bottlenecks: interpretable feature selection and adaptive multi-objective balancing. The LLM analyzes real-time IoT-collected environmental states to extract physically significant features and dynamically assigns weights to conflicting objectives-including profit, user satisfaction, and grid load-using semantic reasoning instead of complex manual tuning. Extensive experiments demonstrate that our framework significantly outperforms state-of-the-art baselines, achieving superior market efficiency while reducing training time by over 70%. This approach offers a scalable, transparent solution for efficient and sustainable IoT-enabled urban charging infrastructure management.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. Builds on 12 of its 59 references that are in the map. First posted on arXiv 12 Sep 2026.

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12 of 59 references.

References · 12

  1. 2024GPT-4o System Cardcited by 4,588
  2. 2023A Survey of Large Language Modelscited by 2,576
  3. 2016Successor Features for Transfer in Reinforcement Learningcited by 340
  4. 2019LassoNet: A Neural Network with Feature Sparsitycited by 82
  5. 2024Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learningcited by 81
  6. 2022LMPriors: Pre-Trained Language Models as Task-Specific Priorscited by 56
  7. 2023Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritizationcited by 48
  8. 2022On Transforming Reinforcement Learning by Transformer: The Development Trajectorycited by 36
  9. 2024LLM-Select: Feature Selection with Large Language Modelscited by 30
  10. 2024Exploring Large Language Models for Feature Selection: A Data-centric Perspectivecited by 27
  11. 2024ICE-SEARCH: A Language Model-Driven Feature Selection Approachcited by 8
  12. 2024Dynamic Pricing for Electric Vehicle Chargingcited by 2

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  2. 2026Smart charging of large fleets of Electric Vehicles: Independent Multi-Agent Reinforcement Learning approaches
  3. 2026Profit Maximization for Electric Vehicle Charging Stations Using Multiagent Reinforcement Learning ↗
  4. 2025Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles
  5. 2025Optimizing Electric Bus Charging Scheduling with Uncertainties Using Hierarchical Deep Reinforcement Learning
  6. 2025Physics-Informed Reinforcement Learning for Large-Scale EV Smart Charging Considering Distribution Network Voltage Constraints
  7. 2024Centralized vs. Decentralized Multi-Agent Reinforcement Learning for Enhanced Control of Electric Vehicle Charging Networks
  8. 2025Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards