Rtaw: An attention inspired reinforcement learning method for multi-robot task allocation in warehouse environments
We present a novel reinforcement learning based algorithm for multi-robot task allocation
problem in ware-house environments. We formulate it as a Markov Decision Process and …
problem in ware-house environments. We formulate it as a Markov Decision Process and …
Deepfreight: A model-free deep-reinforcement-learning-based algorithm for multi-transfer freight delivery
With the freight delivery demands and shipping costs increasing rapidly, intelligent control of
fleets to enable efficient and cost-conscious solutions becomes an important problem. In this …
fleets to enable efficient and cost-conscious solutions becomes an important problem. In this …
Charging cost-aware fleet management for shared on-demand green logistic system
With the advancement of transportation electrification and Internet of Things (IoT), the cloud-
based shared on-demand logistic fleet management platforms, such as Lalamove and …
based shared on-demand logistic fleet management platforms, such as Lalamove and …
A Survey of Machine Learning-Based Ride-Hailing Planning
D Wen, Y Li, FCM Lau - IEEE Transactions on Intelligent …, 2024 - ieeexplore.ieee.org
Ride-hailing is a sustainable transportation paradigm where riders access door-to-door
traveling services through a mobile phone application, which has attracted a colossal …
traveling services through a mobile phone application, which has attracted a colossal …
[HTML][HTML] 考虑工人路径的多智能体强化学习空间众包任务分配方法
纪苗苗， 吴志彬 - 控制与决策, 2024 - kzyjc.alljournals.cn
针对工人和任务进行匹配是空间众包研究的核心问题之一, 但已有的方法通常会忽略工人路径对
任务分配结果产生的影响. 传统的任务分配方法存在计算速度慢, 适用范围小和协作效果不突出 …
任务分配结果产生的影响. 传统的任务分配方法存在计算速度慢, 适用范围小和协作效果不突出 …
Hierarchical Learning with Heuristic Guidance for Multi-task Assignment and Distributed Planning in Interactive Scenarios
S Chen, M Wang, W Song - IEEE Transactions on Intelligent …, 2024 - ieeexplore.ieee.org
A unified framework for multi-agent task assignment and distributed trajectory planning that
can autonomously adapt to complex interactive environments and multi-task constraints has …
can autonomously adapt to complex interactive environments and multi-task constraints has …
Decentralized ride-sharing of shared autonomous vehicles using graph neural network-based reinforcement learning
Ride-sharing has important implications for improving the efficiency of mobility-on-demand
systems. However, it remains a challenge due to the complex dynamics between vehicles …
systems. However, it remains a challenge due to the complex dynamics between vehicles …
Where to go: Agent guidance with deep reinforcement learning in a city-scale online ride-hailing service
Online ride-hailing services have become a prevalent transportation system across the
world. In this paper, we study a challenging problem of how to direct vacant taxis around a …
world. In this paper, we study a challenging problem of how to direct vacant taxis around a …
A Survey of Machine Learning-Based Ride-Hailing Planning
D Wen, Y Li, F Lau - arXiv preprint arXiv:2303.14646, 2023 - arxiv.org
Ride-hailing is a sustainable transportation paradigm where riders access door-to-door
traveling services through a mobile phone application, which has attracted a colossal …
traveling services through a mobile phone application, which has attracted a colossal …
Zone-Agnostic Greedy Taxi Dispatch Algorithm Based on Contextual Matching Matrix for Efficient Maximization of Revenue and Profit
This paper addresses the taxi fleet dispatch problem, which is critical for many transport
service platforms such as Uber, Lyft, and Didi Chuxing. We focus on maximizing the revenue …
service platforms such as Uber, Lyft, and Didi Chuxing. We focus on maximizing the revenue …