# Data-Driven Simulation of Ride-Hailing Services using Imitation and Reinforcement Learning

arXiv: https://arxiv.org/abs/2104.02661 · HTML: https://mlsanity.com/paper/2104.02661/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Imitation and offline RL for driving, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 6 Apr 2021.

## Placed in

- Offline RL and imitation learning from data / [Imitation and offline RL for driving](https://mlsanity.com/offlinerl/imitation-and-offline-rl-for-driving/)

## Builds on (most cited first, 9 of 9)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [A Distributional Perspective on Reinforcement Learning](https://mlsanity.com/paper/1707.06887/) (2017)
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://mlsanity.com/paper/1507.06527/) (2015)
- [The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies](https://mlsanity.com/paper/2004.13332/) (2020)
- [Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments](https://mlsanity.com/paper/1910.04281/) (2019)
- [Optimal Passenger-Seeking Policies on E-hailing Platforms Using Markov Decision Process and Imitation Learning](https://mlsanity.com/paper/1905.09906/) (2019)
- [Modeling Taxi Drivers' Behaviour for the Next Destination Prediction](https://mlsanity.com/paper/1807.08173/) (2018)
- [Driver Surge Pricing](https://mlsanity.com/paper/1905.07544/) (2019)

## Abstract (arXiv)

The rapid growth of ride-hailing platforms has created a highly competitive market where businesses struggle to make profits, demanding the need for better operational strategies. However, real-world experiments are risky and expensive for these platforms as they deal with millions of users daily. Thus, a need arises for a simulated environment where they can predict users' reactions to changes in the platform-specific parameters such as trip fares and incentives. Building such a simulation is challenging, as these platforms exist within dynamic environments where thousands of users regularly interact with one another. This paper presents a framework to mimic and predict user, specifically driver, behaviors in ride-hailing services. We use a data-driven hybrid reinforcement learning and imitation learning approach for this. First, the agent utilizes behavioral cloning to mimic driver behavior using a real-world data set. Next, reinforcement learning is applied on top of the pre-trained agents in a simulated environment, to allow them to adapt to changes in the platform. Our framework provides an ideal playground for ride-hailing platforms to experiment with platform-specific parameters to predict drivers' behavioral patterns.