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arXiv Machine Learning

OpenHail: An Event-Driven Gymnasium Environment for Electric Ride-Hailing Fleet Control

OpenHail is an open-source Gymnasium environment designed for controlling electric ride‑hailing fleets. It offers a fixed‑size observation–action interface that handles request assignment, repositioning, and charging, while its event‑driven simulator models pickup deadlines, vehicle job queues, battery dynamics, and finite‑capacity charging facilities with FIFO queues. The environment supports various decision‑epoch mechanisms—event‑driven, periodic, hybrid, and policy‑requested—allowing flexible policy interactions within a unified operational model, and includes tools for evaluation, metrics, and baseline policies.

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arXiv AI
Sep 4

Towards Affordable Energy: A Gymnasium Environment for Electric Utility Demand-Response Programs

The paper introduces DR‑Gym, an open‑source, Gymnasium‑compatible environment that simulates electric utility demand‑response programs at the market level. It uses a regime‑switching wholesale price model calibrated to real extreme events and physics‑based building demand profiles, providing a rich observational space and a configurable multi‑objective reward function for reinforcement learning. Baseline strategies and data snapshots demonstrate the simulator’s realism and learnability.

By Jose E. Aguilar Escamilla, Lingdong Zhou, Xiangqi Zhu, Huazheng Wang
reinforcement-learning
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arXiv AI
Jul 1

Smart charging of large fleets of Electric Vehicles: Independent Multi-Agent Reinforcement Learning approaches

arXiv:2606. 31347v1 Announce Type: new Abstract: The electrification of transportation through electric vehicles introduces new challenges for power grid management, such as increased peak demand, voltage fluctuations, line overloads, and the integration of variable renewable energy sources.

By Xavier Rate, Eloann Le Guern, Rapha\"el F\'eraud, Fatma Salem, Melissa Chiknoun, Eymeric Giabicani, Mehdi Feki, Patrick Maill\'e, Guy Camilleri, Anne Blavette, Hamid Benhamed
agentsreinforcement-learning
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arXiv Machine Learning
Sep 3

RideSkill: A Hierarchical Algorithm for Generalized Ride Sharing with LLM-Driven Automatic Evolution

RideSkill is a hierarchical algorithm for generalized ride sharing that uses large language models (LLMs) to automatically design and train a skill repository, a combiner, and a repositioner. The combiner assigns vehicle-specific skills for adaptive dispatch across varying scenarios and objectives, while the repositioner moves idle vehicles to emerging regions to avoid conflicts. By training all components via an LLM-based evolutionary method, RideSkill eliminates the need for real-time LLM calls, enabling high-performance deployment in large-scale systems.

By Zijian Zhao, Sen Li, Xialiang Tong, Mingxuan Yuan
llmsagentsreinforcement-learning
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arXiv AI
Jul 3

Sim2Real-AD: A Modular Sim-to-Real Framework for Deploying VLM-Guided Reinforcement Learning in Real-World Autonomous Driving

arXiv:2604. 03497v2 Announce Type: replace-cross Abstract: Vision-language-model (VLM)-guided reinforcement learning (RL) has recently attracted significant attention for it, replacing brittle hand-crafted rewards with semantically grounded signals; however, deploying such simulation-trained policies on real vehicles remains a fundamental challenge, because they rely on simulator-native observations and simulator-coupled action semantics with no counterpart on physical hardware.

By Zilin Huang, Zhengyang Wan, Zihao Sheng, Boyue Wang, Junwei You, Sikai Chen
agentsreinforcement-learningroboticsmultimodal
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arXiv AI
Jun 18

A Distributionally Robust Reinforcement Learning Framework for Constrained Urban EV Dispatch

arXiv:2604. 25848v2 Announce Type: replace Abstract: We study city-scale control of electric-vehicle (EV) ride-hailing fleets where dispatch, repositioning, and charging decisions must respect charger and feeder limits under uncertain, spatially correlated demand and travel times.

By An Nguyen, Hoang Nguyen, Phuong Le, Hung Pham, Cuong Do, Laurent El Ghaoui
agentsreinforcement-learning
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arXiv AI
Sep 15

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

The paper introduces a Large Language Model–enhanced Multi-Agent Reinforcement Learning framework for optimizing electric vehicle charging, station profitability, and grid stability in public charging systems. By using an LLM to select interpretable features from IoT data and dynamically balance conflicting objectives, the approach unifies grid, EV, and station optimization in a single loop. Experiments show the method outperforms existing baselines, improving market efficiency and cutting training time by more than 70%.

By Yang Zhang, Lindong Xie, Chongyu Wang, Gaojunjie Li, Siqi Bu, Edward Chung
llmsagentsreinforcement-learningbenchmarks
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