EV charging station deployment on coupled transportation and power distribution networks via reinforcement learning
This study addresses the optimal electric vehicle (EV) charging station deployment problem
(CSDP) on coupled transportation and power distribution networks, which is one of the …
(CSDP) on coupled transportation and power distribution networks, which is one of the …
Multi-objective deep reinforcement learning assisted service function chains placement
Y Bi, CC Meixner, M Bunyakitanon… - … on Network and …, 2021 - ieeexplore.ieee.org
The study of Service Function Chains (SFCs) placement problem is crucial to support
services flexibly and use resources efficiently. Solutions should satisfy various Quality of …
services flexibly and use resources efficiently. Solutions should satisfy various Quality of …
Real-time neural network scheduling of emergency medical mask production during COVID-19
During the outbreak of the novel coronavirus pneumonia (COVID-19), there is a huge
demand for medical masks. A mask manufacturer often receives a large amount of orders …
demand for medical masks. A mask manufacturer often receives a large amount of orders …
Deep reinforcement learning for robust vnf reconfigurations in o-ran
Open Radio Access Networks (O-RANs) have revolutionized the telecom ecosystem by
bringing intelligence into disaggregated RAN and implementing functionalities as Virtual …
bringing intelligence into disaggregated RAN and implementing functionalities as Virtual …
Glsearch: Maximum common subgraph detection via learning to search
Abstract Detecting the Maximum Common Subgraph (MCS) between two input graphs is
fundamental for applications in drug synthesis, malware detection, cloud computing, etc …
fundamental for applications in drug synthesis, malware detection, cloud computing, etc …
George: Learning to place long-lived containers in large clusters with operation constraints
Online cloud services are widely deployed as Long-Running Applications (LRAs) hosted in
containers. Placing LRA containers turns out to be particularly challenging due to the …
containers. Placing LRA containers turns out to be particularly challenging due to the …
Learning to generalize Dispatching rules on the Job Shop Scheduling
This paper introduces a Reinforcement Learning approach to better generalize heuristic
dispatching rules on the Job-shop Scheduling Problem (JSP). Current models on the JSP do …
dispatching rules on the Job-shop Scheduling Problem (JSP). Current models on the JSP do …
Constrained deep reinforcement based functional split optimization in virtualized RANs
In virtualized radio access network (vRAN), the base station (BS) functions are decomposed
into virtualized components that can be hosted at the centralized unit or distributed units …
into virtualized components that can be hosted at the centralized unit or distributed units …
Risk-Aware Continuous Control with Neural Contextual Bandits
JA Ayala-Romero, A Garcia-Saavedra… - Proceedings of the …, 2024 - ojs.aaai.org
Recent advances in learning techniques have garnered attention for their applicability to a
diverse range of real-world sequential decision-making problems. Yet, many practical …
diverse range of real-world sequential decision-making problems. Yet, many practical …
Assessment of reinforcement learning algorithms for nuclear power plant fuel optimization
The nuclear fuel loading pattern optimization problem belongs to the class of large-scale
combinatorial optimization. It is also characterized by multiple objectives and constraints …
combinatorial optimization. It is also characterized by multiple objectives and constraints …