# A Better Match for Drivers and Riders: Reinforcement Learning at Lyft

arXiv: https://arxiv.org/abs/2310.13810 · HTML: https://mlsanity.com/paper/2310.13810/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 20 Oct 2023.

## Builds on (most cited first, 5 of 5)

- [A Deep Value-network Based Approach for Multi-Driver Order Dispatching](https://mlsanity.com/paper/2106.04493/) (2021)
- [Reinforcement Learning for Ridesharing: An Extended Survey](https://mlsanity.com/paper/2105.01099/) (2021)
- [Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem](https://mlsanity.com/paper/1911.11260/) (2019)
- [Dynamic Type Matching](https://mlsanity.com/paper/1811.07048/) (2018)
- [Dynamic Spatial Matching](https://mlsanity.com/paper/2105.07329/) (2021)

## Built on by (most cited first, 9 of 9)

- [Reinitializing weights vs units for maintaining plasticity in neural networks](https://mlsanity.com/paper/2508.00212/) (2025)
- [Almost Sure Convergence of Linear Temporal Difference Learning with Arbitrary Features](https://mlsanity.com/paper/2409.12135/) (2024)
- [Reinforcement Learning for Intensity Control: An Application to Choice-Based Network Revenue Management](https://mlsanity.com/paper/2406.05358/) (2024)
- [The Formalism-Implementation Gap in Reinforcement Learning Research](https://mlsanity.com/paper/2510.16175/) (2025)
- [Let's Have a Conversation: Designing and Evaluating LLM Agents for Interactive Optimization](https://mlsanity.com/paper/2604.02666/) (2026)
- [The New Era of Dynamic Pricing: Synergizing Supervised Learning and Quadratic Programming](https://mlsanity.com/paper/2402.14844/) (2024)
- [Multi-agent Markov Entanglement](https://mlsanity.com/paper/2506.02385/) (2025)
- [Dynamic Vehicle Routing Problem with Prompt Confirmation of Advance Requests](https://mlsanity.com/paper/2603.07422/) (2026)
- [Learn to Match: Two-Sided Matching with Temporally Extended Feedback](https://mlsanity.com/paper/2606.06744/) (2026)

## Abstract (arXiv)

To better match drivers to riders in our ridesharing application, we revised Lyft's core matching algorithm. We use a novel online reinforcement learning approach that estimates the future earnings of drivers in real time and use this information to find more efficient matches. This change was the first documented implementation of a ridesharing matching algorithm that can learn and improve in real time. We evaluated the new approach during weeks of switchback experimentation in most Lyft markets, and estimated how it benefited drivers, riders, and the platform. In particular, it enabled our drivers to serve millions of additional riders each year, leading to more than 30 million per year in incremental revenue. Lyft rolled out the algorithm globally in 2021.