# Dual Policy Reinforcement Learning for Real-time Rebalancing in Bike-sharing Systems

arXiv: https://arxiv.org/abs/2406.00868 · HTML: https://mlsanity.com/paper/2406.00868/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 2 Jun 2024.

## 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, 7 of 7)

- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [A Review of Cooperative Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/1908.03963/) (2019)
- [Comparison of non-linear activation functions for deep neural networks on MNIST classification task](https://mlsanity.com/paper/1804.02763/) (2018)
- [Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control](https://mlsanity.com/paper/2302.03963/) (2023)
- [A Distributed Reinforcement Learning Solution With Knowledge Transfer Capability for A Bike Rebalancing Problem](https://mlsanity.com/paper/1810.04058/) (2018)
- [Single- vs. Dual-Policy Reinforcement Learning for Dynamic Bike Rebalancing](https://mlsanity.com/paper/2402.03589/) (2024)

## Built on by (most cited first, 2 of 2)

- [Deep RL Dual Sourcing Inventory Management with Supply and Capacity Risk Awareness](https://mlsanity.com/paper/2507.14446/) (2025)
- [SmartFlow Reinforcement Learning and Agentic AI for Bike-Sharing Optimisation](https://mlsanity.com/paper/2601.00868/) (2025)

## Abstract (arXiv)

Bike-sharing systems play a crucial role in easing traffic congestion and promoting healthier lifestyles. However, ensuring their reliability and user acceptance requires effective strategies for rebalancing bikes. This study introduces a novel approach to address the real-time rebalancing problem with a fleet of vehicles. It employs a dual policy reinforcement learning algorithm that decouples inventory and routing decisions, enhancing realism and efficiency compared to previous methods where both decisions were made simultaneously. We first formulate the inventory and routing subproblems as a multi-agent Markov Decision Process within a continuous time framework. Subsequently, we propose a DQN-based dual policy framework to jointly estimate the value functions, minimizing the lost demand. To facilitate learning, a comprehensive simulator is applied to operate under a first-arrive-first-serve rule, which enables the computation of immediate rewards across diverse demand scenarios. We conduct extensive experiments on various datasets generated from historical real-world data, affected by both temporal and weather factors. Our proposed algorithm demonstrates significant performance improvements over previous baseline methods. It offers valuable practical insights for operators and further explores the incorporation of reinforcement learning into real-world dynamic programming problems, paving the way for more intelligent and robust urban mobility solutions.