Machine learning in aerodynamic shape optimization
Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …
optimization (ASO), thanks to the availability of aerodynamic data and continued …
Deep reinforcement learning for the control of robotic manipulation: a focussed mini-review
Deep learning has provided new ways of manipulating, processing and analyzing data. It
sometimes may achieve results comparable to, or surpassing human expert performance …
sometimes may achieve results comparable to, or surpassing human expert performance …
Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities
With advances in technologies, data science techniques, and computing equipment, there
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …
Reinforcement learning applied to production planning and control
The objective of this paper is to examine the use and applications of reinforcement learning
(RL) techniques in the production planning and control (PPC) field addressing the following …
(RL) techniques in the production planning and control (PPC) field addressing the following …
Deep reinforcement learning for the dynamic and uncertain vehicle routing problem
W Pan, SQ Liu - Applied Intelligence, 2023 - Springer
Accurate and real-time tracking for real-world urban logistics has become a popular
research topic in the field of intelligent transportation. While the routing of urban logistic …
research topic in the field of intelligent transportation. While the routing of urban logistic …
Challenges and opportunities in deep reinforcement learning with graph neural networks: A comprehensive review of algorithms and applications
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
Hierarchical graph multi-agent reinforcement learning for traffic signal control
S Yang - Information Sciences, 2023 - Elsevier
Multi-agent reinforcement learning (MARL) is a promising algorithm for traffic signal control
(TSC), and graph neural networks make a further improvement on its learning capacity …
(TSC), and graph neural networks make a further improvement on its learning capacity …
A review on learning to solve combinatorial optimisation problems in manufacturing
An efficient manufacturing system is key to maintaining a healthy economy today. With the
rapid development of science and technology and the progress of human society, the …
rapid development of science and technology and the progress of human society, the …
Modeling driver's evasive behavior during safety–critical lane changes: Two-dimensional time-to-collision and deep reinforcement learning
Lane changes are complex driving behaviors and frequently involve safety–critical
situations. This study aims to develop a lane-change-related evasive behavior model, which …
situations. This study aims to develop a lane-change-related evasive behavior model, which …
Adaptive speed planning of connected and automated vehicles using multi-light trained deep reinforcement learning
Through shared real-time traffic information and perception of complex environments,
connected and automated vehicles (CAVs) are endowed with global decision-making …
connected and automated vehicles (CAVs) are endowed with global decision-making …