# A Distributed Reinforcement Learning Solution With Knowledge Transfer Capability for A Bike Rebalancing Problem

arXiv: https://arxiv.org/abs/1810.04058 · HTML: https://mlsanity.com/paper/1810.04058/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2018.

## Built on by (most cited first, 3 of 3)

- [Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning](https://mlsanity.com/paper/2105.14780/) (2021)
- [Single- vs. Dual-Policy Reinforcement Learning for Dynamic Bike Rebalancing](https://mlsanity.com/paper/2402.03589/) (2024)
- [Dual Policy Reinforcement Learning for Real-time Rebalancing in Bike-sharing Systems](https://mlsanity.com/paper/2406.00868/) (2024)

## Abstract (arXiv)

Rebalancing is a critical service bottleneck for many transportation services, such as Citi Bike. Citi Bike relies on manual orchestrations of rebalancing bikes between dispatchers and field agents. Motivated by such problem and the lack of smart autonomous solutions in this area, this project explored a new RL architecture called Distributed RL (DiRL) with Transfer Learning (TL) capability. The DiRL solution is adaptive to changing traffic dynamics when keeping bike stock under control at the minimum cost. DiRL achieved a 350% improvement in bike rebalancing autonomously and TL offered a 62.4% performance boost in managing an entire bike network. Lastly, a field trip to the dispatch office of Chariot, a ride-sharing service, provided insights to overcome challenges of deploying an RL solution in the real world.