Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2006.10897v1
• Date: Thu, 18 Jun 2020 23:37:53 GMT
• Title: Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement Learning
• Authors: Oscar de Lima, Hansal Shah, Ting-Sheng Chu, Brian Fogelson
• Abstract summary: Ride-sharing services such as Uber and Lyft offer a service where passengers can order a car to pick them up. Traditional Reinforcement Learning (RL) based methods attempting to solve the ridesharing problem are unable to accurately model the complex environment in which taxis operate. 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. Our algorithm is able to outperform IDQN baseline in the scenario where we have a variable number of passengers and cars in each episode.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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