论文标题
使用多代理强化学习有效的乘车分机调度
Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement Learning
论文作者
论文摘要
随着乘车共享服务的出现，依靠他们满足各种需求的人数大大增加。解决此问题的大多数方法都需要手工制作的功能来估计旅行时间和乘客等待时间。试图解决乘车问题的基于传统的加固学习（RL）方法无法准确地对出租车运行的复杂环境进行建模。基于独立DQN（IDQN）的先前基于多代理的深度RL方法，由于对多种代理的同时学习和探索，因此学习了容易发生不稳定性的分散价值函数。我们提出的基于QMIX的方法能够通过分散执行实现集中式培训。我们表明，我们的模型在固定的网格大小上的性能优于IDQN基线，并且能够将其概括为较小或更大的网格尺寸。此外，在每个情节中，我们的乘客和汽车数量可变，我们的算法能够优于IDQN基线。我们的论文代码可在以下网址公开获取：https：//github.com/umich-ml-group/rl-ridesharing。
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.
