自主应需移动系统混合多智能体深度强化学习

来自今天的爱可可AI前沿推介

[LG] Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems

T Enders, J Harrison, M Pavone, M Schiffer
[Technical University of Munich & Google Research & Stanford University]

自主应需移动系统混合多智能体深度强化学习

要点：

1. 提出一种新方法，对自动应需移动系统运营商原本难以解决的行动空间进行分解，同时仍然获得全局协调的决策；
2. 基于真实世界的数据进行的实验表明，该方法在性能、稳定性和计算易用性方面优于多种最先进的基准测试。

摘要：
本文考虑为自主移动需求系统的利润最大化的运营商做出主动请求分配和拒绝决定的顺序决策问题。将这一问题形式化为马尔可夫决策过程，提出一种新的多智能体软Actor-Critic和加权两面匹配的组合，以获得一种预期的控制策略。将运营商原本难以解决的行动空间进行了分解，但仍然获得了一个全局协调的决策。基于现实世界出租车数据的实验表明，所提出方法在性能、稳定性和计算可操作性方面优于现有的基准。

We consider the sequential decision-making problem of making proactive request assignment and rejection decisions for a profit-maximizing operator of an autonomous mobility on demand system. We formalize this problem as a Markov decision process and propose a novel combination of multi-agent Soft Actor-Critic and weighted bipartite matching to obtain an anticipative control policy. Thereby, we factorize the operator's otherwise intractable action space, but still obtain a globally coordinated decision. Experiments based on real-world taxi data show that our method outperforms state of the art benchmarks with respect to performance, stability, and computational tractability.

论文链接：https://arxiv.org/abs/2212.07313