Virtual network embedding based on hierarchical cooperative multi-agent reinforcement learning
Virtual network embedding (VNE) is a promising technique enabling 5G networks to satisfy
the given requirements of each service via network virtualization (NV). For better …
the given requirements of each service via network virtualization (NV). For better …
A hierarchical deep reinforcement learning model with expert prior knowledge for intelligent penetration testing
Q Li, M Zhang, Y Shen, R Wang, M Hu, Y Li, H Hao - Computers & Security, 2023 - Elsevier
Penetration testing (PT) is an effective method to assess the security of a network, mainly
carried out by experienced human experts, and is widely applied in practice. It is urgent to …
carried out by experienced human experts, and is widely applied in practice. It is urgent to …
Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectives
Although several surveys on Neural Combinatorial Optimization (NCO) solvers specifically
designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing …
designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing …
REDE: Exploring Relay Transportation for Efficient Last-mile Delivery
Last-mile delivery from delivery stations to customers' places is now mainly finished by
dedicated couriers. In practice, each courier generally collects orders destined for one …
dedicated couriers. In practice, each courier generally collects orders destined for one …
Efficient Planning with Latent Diffusion
W Li - arXiv preprint arXiv:2310.00311, 2023 - arxiv.org
Temporal abstraction and efficient planning pose significant challenges in offline
reinforcement learning, mainly when dealing with domains that involve temporally extended …
reinforcement learning, mainly when dealing with domains that involve temporally extended …
An end-to-end hierarchical reinforcement learning framework for large-scale dynamic flexible job-shop scheduling problem
The dynamic flexible job shop scheduling problem (DFJSP) is frequently encountered in the
modern manufacturing industry. As the intelligent manufacturing paradigm evolves, it is …
modern manufacturing industry. As the intelligent manufacturing paradigm evolves, it is …
Pandr: Fast adaptation to new environments from offline experiences via decoupling policy and environment representations
Deep Reinforcement Learning (DRL) has been a promising solution to many complex
decision-making problems. Nevertheless, the notorious weakness in generalization among …
decision-making problems. Nevertheless, the notorious weakness in generalization among …
Large-scale dynamic surgical scheduling under uncertainty by hierarchical reinforcement learning
L Zhao, H Zhu, M Zhang, J Tang… - International Journal of …, 2024 - Taylor & Francis
Dynamic surgical scheduling within a workday is a complicated decision-making process.
The critical challenge is that the actual duration of surgery and the arrival process of …
The critical challenge is that the actual duration of surgery and the arrival process of …
Introduction to the dynamic pickup and delivery problem benchmark--ICAPS 2021 competition
The Dynamic Pickup and Delivery Problem (DPDP) is an essential problem within the
logistics domain. So far, research on this problem has mainly focused on using artificial data …
logistics domain. So far, research on this problem has mainly focused on using artificial data …
DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAV
Exploiting unmanned aerial vehicles (UAVs) to execute tasks is gaining growing popularity
recently. To address the underlying task scheduling problem, conventional exact and …
recently. To address the underlying task scheduling problem, conventional exact and …