# Bridging Policy and Real-World Dynamics: LLM-Augmented Rebalancing for Shared Micromobility Systems

arXiv: https://arxiv.org/abs/2603.00176 · HTML: https://mlsanity.com/paper/2603.00176/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 38 references that are in the map.
- First posted on arXiv 26 Feb 2026.

## Builds on (most cited first, 17 of 17)

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- [AdaPlanner: Adaptive Planning from Feedback with Language Models](https://mlsanity.com/paper/2305.16653/) (2023)
- [Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning](https://mlsanity.com/paper/2410.21845/) (2024)
- [Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization](https://mlsanity.com/paper/2308.02151/) (2023)
- [Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment](https://mlsanity.com/paper/2211.08416/) (2022)
- [HAIM-DRL: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Driving](https://mlsanity.com/paper/2401.03160/) (2024)
- [Human-in-the-Loop Deep Reinforcement Learning with Application to Autonomous Driving](https://mlsanity.com/paper/2104.07246/) (2021)
- [A Robust and Constrained Multi-Agent Reinforcement Learning Electric Vehicle Rebalancing Method in AMoD Systems](https://mlsanity.com/paper/2209.08230/) (2022)
- [Routine: A Structural Planning Framework for LLM Agent System in Enterprise](https://mlsanity.com/paper/2507.14447/) (2025)
- [Robust Electric Vehicle Balancing of Autonomous Mobility-On-Demand System: A Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2307.16228/) (2023)
- [MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctions](https://mlsanity.com/paper/2408.09768/) (2024)
- [Autonomous Vehicle Public Transportation System: Scheduling and Admission Control](https://mlsanity.com/paper/1502.07242/) (2015)
- [Urban-Focused Multi-Task Offline Reinforcement Learning with Contrastive Data Sharing](https://mlsanity.com/paper/2406.14054/) (2024)
- [LLM-Guided Reinforcement Learning: Addressing Training Bottlenecks through Policy Modulation](https://mlsanity.com/paper/2505.20671/) (2025)
- [REALISM: A Regulatory Framework for Coordinated Scheduling in Multi-Operator Shared Micromobility Services](https://mlsanity.com/paper/2508.10166/) (2025)
- [Robust Vehicle Rebalancing with Deep Uncertainty in Autonomous Mobility-on-Demand Systems](https://mlsanity.com/paper/2507.04520/) (2025)
- [Small Fleet, Big Impact: Enhancing Shared Micromobility Efficiency through Minimal Autonomous Vehicle Deployment](https://mlsanity.com/paper/2510.04271/) (2025)

## Abstract (arXiv)

Shared micromobility services such as e-scooters and bikes have become an integral part of urban transportation, yet their efficiency critically depends on effective vehicle rebalancing. Existing methods either optimize for average demand patterns or employ robust optimization and reinforcement learning to handle predefined uncertainties. However, these approaches overlook emergent events (e.g., demand surges, vehicle outages, regulatory interventions) or sacrifice performance in normal conditions. We introduce AMPLIFY, an LLM-augmented policy adaptation framework for shared micromobility rebalancing. The framework combines a baseline rebalancing module with an LLM-based adaptation module that adjusts strategies in real time under emergent scenarios. The adaptation module ingests system context, demand predictions, and baseline strategies, and refines adjustments through self-reflection. Evaluations on real-world e-scooter data from Chicago show that our approach improves demand satisfaction and system revenue compared to baseline policies, highlighting the potential of LLM-driven adaptation as a flexible solution for managing uncertainty in micromobility systems.