# Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2605.14501 · HTML: https://mlsanity.com/paper/2605.14501/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 14 May 2026.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 8 of 8)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems](https://mlsanity.com/paper/1802.04592/) (2018)
- [Efficient Sensors Selection for Traffic Flow Monitoring: An Overview of Model-Based Techniques leveraging Network Observability](https://mlsanity.com/paper/2404.08588/) (2024)
- [Visual Sensor Network Stimulation Model Identification via Gaussian Mixture Model and Deep Embedded Features](https://mlsanity.com/paper/2201.06804/) (2022)
- [A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Services](https://mlsanity.com/paper/2403.15780/) (2024)

## Abstract (arXiv)

This paper proposes a fully dynamic Deep Reinforcement Learning (DRL) method for rebalancing dockless bike-sharing systems, overcoming the limitations of periodic, system-wide interventions. We model the service through a graph-based simulator and cast rebalancing as a Markov decision process. A DRL agent routes a single truck in real time, executing localized pick-up, drop-off, and charging actions guided by spatiotemporal criticality scores. Experiments on real-world data show significant reductions in availability failures with a minimal fleet size, while limiting spatial inequality and mobility deserts. Our approach demonstrates the value of learning-based rebalancing for efficient and reliable shared micromobility.