# Single- vs. Dual-Policy Reinforcement Learning for Dynamic Bike Rebalancing

arXiv: https://arxiv.org/abs/2402.03589 · HTML: https://mlsanity.com/paper/2402.03589/

## 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 9 of its 36 references that are in the map.
- First posted on arXiv 5 Feb 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, 9 of 9)

- [Attention, Learn to Solve Routing Problems!](https://mlsanity.com/paper/1803.08475/) (2018)
- [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 Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems](https://mlsanity.com/paper/1802.04592/) (2018)
- [A Distributed Reinforcement Learning Solution With Knowledge Transfer Capability for A Bike Rebalancing Problem](https://mlsanity.com/paper/1810.04058/) (2018)
- [Repositioning Bikes with Carrier Vehicles and Bike Trailers in Bike Sharing Systems](https://mlsanity.com/paper/1909.09616/) (2019)

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

- [Dual Policy Reinforcement Learning for Real-time Rebalancing in Bike-sharing Systems](https://mlsanity.com/paper/2406.00868/) (2024)
- [Deep RL Dual Sourcing Inventory Management with Supply and Capacity Risk Awareness](https://mlsanity.com/paper/2507.14446/) (2025)

## Abstract (arXiv)

Bike-sharing systems (BSS) provide a sustainable urban mobility solution, but ensuring their reliability requires effective rebalancing strategies to address stochastic demand and prevent station imbalances. This paper proposes reinforcement learning (RL) algorithms for dynamic rebalancing problem with multiple vehicles, introducing and comparing two RL approaches: Single-policy RL and Dual-policy RL. We formulate this network optimization problem as a Markov Decision Process within a continuous-time framework, allowing vehicles to make independent and cooperative rebalancing decisions without synchronization constraints. In the first approach, a single deep Q-network (DQN) is trained to jointly learn inventory and routing decisions. The second approach decouples node-level inventory decisions from arc-level vehicle routing, enhancing learning efficiency and adaptability. A high-fidelity simulator under the first-arrive-first-serve rule is developed to estimate rewards across diverse demand scenarios influenced by temporal and weather variations. Extensive experiments demonstrate that while the single-policy model is competitive against several benchmarks, the dual-policy model significantly reduces lost demand. These findings provide valuable insights for bike-sharing operators, reinforcing the potential of RL for real-time rebalancing and paving the way for more adaptive and intelligent urban mobility solutions.