Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement Learning

2020-06-18 · Oscar de Lima, Hansal Shah, Ting-Sheng Chu, Brian Fogelson

With the advent of ride-sharing services, there is a huge increase in the number of people who rely on them for various needs. Most of the earlier approaches tackling this issue required handcrafted functions for estimating travel times and passenger waiting times. Traditional Reinforcement Learning (RL) based methods attempting to solve the ridesharing problem are unable to accurately model the complex environment in which taxis operate. Prior Multi-Agent Deep RL based methods based on Independent DQN (IDQN) learn decentralized value functions prone to instability due to the concurrent learning and exploring of multiple agents. Our proposed method based on QMIX is able to achieve centralized training with decentralized execution. We show that our model performs better than the IDQN baseline on a fixed grid size and is able to generalize well to smaller or larger grid sizes. Also, our algorithm is able to outperform IDQN baseline in the scenario where we have a variable number of passengers and cars in each episode. Code for our paper is publicly available at: https://github.com/UMich-ML-Group/RL-Ridesharing.

📄 PDF Abstract BibTeX arXiv:2006.10897

Code (1)

UMich-ML-Group/RL-Ridesharing 공식 구현 pytorch

Tasks

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Methods 이 논문이 사용한 방법론

Travel 설명 없음

Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…

Q-Learning Q-Learning is an off-policy temporal difference control algorithm: $$Q\left(S\_{t}, A\_{t}\right) \leftarrow Q\left(S\_{t}, A\_{t}\right) + \alpha\left[R_{t+1} +…

Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…

DQN A DQN, or Deep Q-Network, approximates a state-value function in a Q-Learning framework with a neural network. In the Atari…

Spatio-Temporal Hierarchical Adaptive Dispatching for Ridesharing Systems

2020-09-04 · Chang Liu, Jiahui Sun, Haiming Jin, Meng Ai 외

Nowadays, ridesharing has become one of the most popular services offered by online ride-hailing platforms (e.g., Uber and Didi Chuxing). Existing ridesharing platforms adopt the strategy that dispatches orders over the …

MOVI: A Model-Free Approach to Dynamic Fleet Management

2018-04-13 · Takuma Oda, Carlee Joe-Wong

Modern vehicle fleets, e.g., for ridesharing platforms and taxi companies, can reduce passengers' waiting times by proactively dispatching vehicles to locations where pickup requests are anticipated in the future. Yet it…

Management

Reinforcement Learning for Ridesharing: An Extended Survey

2021-05-03 · Zhiwei Qin, Hongtu Zhu, Jieping Ye

In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement learning approaches to decision optimization problems in a typical ridesharing system. Papers on the topics of rideshare match…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Survey

A Unified Model for the Two-stage Offline-then-Online Resource Allocation

2020-12-12 · Yifan Xu, Pan Xu, Jianping Pan, Jun Tao

With the popularity of the Internet, traditional offline resource allocation has evolved into a new form, called online resource allocation. It features the online arrivals of agents in the system and the real-time decis…

Decision Making

A Distributed Model-Free Algorithm for Multi-hop Ride-sharing using Deep Reinforcement Learning

2019-10-30 · Ashutosh Singh, Abubakr Alabbasi, Vaneet Aggarwal

The growth of autonomous vehicles, ridesharing systems, and self driving technology will bring a shift in the way ride hailing platforms plan out their services. However, these advances in technology coupled with road co…

Autonomous Vehicles Deep Reinforcement Learning Reinforcement Learning Reinforcement Learning (RL)