Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement Learning
This work addresses the efficiency of ride-sharing services, which is an incremental improvement over prior methods for optimizing dispatch in transportation systems.
The paper tackles the ridesharing dispatch problem by proposing a multi-agent reinforcement learning method based on QMIX, which outperforms an Independent DQN baseline in terms of performance and generalization across different grid sizes and variable numbers of passengers and cars.
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.