  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.
