# DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1903.03882 · HTML: https://mlsanity.com/paper/1903.03882/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2019.

## Built on by (most cited first, 30 of 54)

- [Reinforcement Learning for Ridesharing: An Extended Survey](https://mlsanity.com/paper/2105.01099/) (2021)
- [On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC)](https://mlsanity.com/paper/2109.04024/) (2021)
- [Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes](https://mlsanity.com/paper/2309.01922/) (2023)
- [Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning](https://mlsanity.com/paper/2103.04555/) (2021)
- [Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates](https://mlsanity.com/paper/2310.19807/) (2023)
- [Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis](https://mlsanity.com/paper/2404.08003/) (2024)
- [Multi-task Hierarchical Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/2305.12633/) (2023)
- [A Sharper Global Convergence Analysis for Average Reward Reinforcement Learning via an Actor-Critic Approach](https://mlsanity.com/paper/2407.18878/) (2024)
- [Sample-Efficient Constrained Reinforcement Learning with General Parameterization](https://mlsanity.com/paper/2405.10624/) (2024)
- [Communication Efficient Parallel Reinforcement Learning](https://mlsanity.com/paper/2102.10740/) (2021)
- [Learning General Parameterized Policies for Infinite Horizon Average Reward Constrained MDPs via Primal-Dual Policy Gradient Algorithm](https://mlsanity.com/paper/2402.02042/) (2024)
- [Mean-Field Control based Approximation of Multi-Agent Reinforcement Learning in Presence of a Non-decomposable Shared Global State](https://mlsanity.com/paper/2301.06889/) (2023)
- [Global Convergence for Average Reward Constrained MDPs with Primal-Dual Actor Critic Algorithm](https://mlsanity.com/paper/2505.15138/) (2025)
- [A Unified Algorithm Framework for Unsupervised Discovery of Skills based on Determinantal Point Process](https://mlsanity.com/paper/2212.00211/) (2022)
- [Model-Based Transfer Learning for Contextual Reinforcement Learning](https://mlsanity.com/paper/2408.04498/) (2024)
- [BMG-Q: Localized Bipartite Match Graph Attention Q-Learning for Ride-Pooling Order Dispatch](https://mlsanity.com/paper/2501.13448/) (2025)
- [Finite-Sample Analysis of Policy Evaluation for Robust Average Reward Reinforcement Learning](https://mlsanity.com/paper/2502.16816/) (2025)
- [Regret Analysis of Average-Reward Unichain MDPs via an Actor-Critic Approach](https://mlsanity.com/paper/2505.19986/) (2025)
- [A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem](https://mlsanity.com/paper/2105.13284/) (2021)
- [Multi-Agent Transfer Learning in Reinforcement Learning-Based Ride-Sharing Systems](https://mlsanity.com/paper/2112.00424/) (2021)
- [Can Mean Field Control (MFC) Approximate Cooperative Multi Agent Reinforcement Learning (MARL) with Non-Uniform Interaction?](https://mlsanity.com/paper/2203.00035/) (2022)
- [Quantum Computing Provides Exponential Regret Improvement in Episodic Reinforcement Learning](https://mlsanity.com/paper/2302.08617/) (2023)
- [Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries](https://mlsanity.com/paper/2403.09940/) (2024)
- [The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication](https://mlsanity.com/paper/2103.13026/) (2021)
- [Mean-Field Approximation of Cooperative Constrained Multi-Agent Reinforcement Learning (CMARL)](https://mlsanity.com/paper/2209.07437/) (2022)
- [Stochastic Q-learning for Large Discrete Action Spaces](https://mlsanity.com/paper/2405.10310/) (2024)
- [One Step is Enough: Multi-Agent Reinforcement Learning based on One-Step Policy Optimization for Order Dispatch on Ride-Sharing Platforms](https://mlsanity.com/paper/2507.15351/) (2025)
- [AdaPool: A Diurnal-Adaptive Fleet Management Framework using Model-Free Deep Reinforcement Learning and Change Point Detection](https://mlsanity.com/paper/2104.00203/) (2021)
- [On the Near-Optimality of Local Policies in Large Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2209.03491/) (2022)
- [Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach](https://mlsanity.com/paper/2501.16243/) (2025)

## Abstract (arXiv)

The success of modern ride-sharing platforms crucially depends on the profit of the ride-sharing fleet operating companies, and how efficiently the resources are managed. Further, ride-sharing allows sharing costs and, hence, reduces the congestion and emission by making better use of vehicle capacities. In this work, we develop a distributed model-free, DeepPool, that uses deep Q-network (DQN) techniques to learn optimal dispatch policies by interacting with the environment. Further, DeepPool efficiently incorporates travel demand statistics and deep learning models to manage dispatching vehicles for improved ride sharing services. Using real-world dataset of taxi trip records in New York City, DeepPool performs better than other strategies, proposed in the literature, that do not consider ride sharing or do not dispatch the vehicles to regions where the future demand is anticipated. Finally, DeepPool can adapt rapidly to dynamic environments since it is implemented in a distributed manner in which each vehicle solves its own DQN individually without coordination.