mlsanityData to 7 Oct 2026

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

arXiv 2104.02661 ↗First posted 6 Apr 2021cs.LG cs.AIcited by 0

On the map: Offline RL and imitation learning from data → Imitation and offline RL for driving

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.

Key facts. No citing papers in the map yet (data through 7 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.

Builds on

9 of 16 references.

References · 9

  1. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  2. 2017A Distributional Perspective on Reinforcement Learningcited by 743
  3. 2018Behavioral Cloning from Observationcited by 482
  4. 2015Deep Recurrent Q-Learning for Partially Observable MDPscited by 457
  5. 2020The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policiescited by 75
  6. 2019Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environmentscited by 17
  7. 2019Optimal Passenger-Seeking Policies on E-hailing Platforms Using Markov Decision Process and Imitation Learningcited by 10
  8. 2018Modeling Taxi Drivers' Behaviour for the Next Destination Predictioncited by 4
  9. 2019Driver Surge Pricingcited by 4

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  1. 2026ALIGN-HOLD: Experience Alignment for Real-Time Hold Control in Large-Scale Ride-Hailing Matching at DiDi
  2. 2022Spatio-temporal Incentives Optimization for Ride-hailing Services with Offline Deep Reinforcement Learning
  3. 2025Group Effect Enhanced Generative Adversarial Imitation Learning for Individual Travel Behavior Modeling under Incentives
  4. 2025RAST-MoE-RL: A Regime-Aware Spatio-Temporal MoE Framework for Deep Reinforcement Learning in Ride-Hailing
  5. 2026D3-Subsidy: Online and Sequential Driver Subsidy Decision-Making for Large-Scale Ride-Hailing Market
  6. 2024Imitating Cost-Constrained Behaviors in Reinforcement Learning
  7. 2019Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation Learning
  8. 2025Exposing the Copycat Problem of Imitation-based Planner: A Novel Closed-Loop Simulator, Causal Benchmark and Joint IL-RL Baseline