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.

中文翻译：

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使用多代理强化学习的高效拼车调度

随着拼车服务的出现，依赖它们满足各种需求的人数大幅增加。大多数早期解决这个问题的方法都需要手工制作的函数来估计旅行时间和乘客等待时间。试图解决拼车问题的传统强化学习 (RL) 方法无法准确模拟出租车运营的复杂环境。基于独立 DQN (IDQN) 的先前基于多智能体深度强化学习的方法学习分散的价值函数，由于多个智能体的并发学习和探索而容易不稳定。我们提出的基于 QMIX 的方法能够实现集中训练和分散执行。我们表明，我们的模型在固定网格尺寸上的性能优于 IDQN 基线，并且能够很好地推广到更小或更大的网格尺寸。此外，我们的算法能够在每集中有可变数量的乘客和汽车的场景中优于 IDQN 基线。我们论文的代码可在以下网址公开获取：https://github.com/UMich-ML-Group/RL-Ridesharing。