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 …
A review of deep reinforcement learning for smart building energy management
Global buildings account for about 30% of the total energy consumption and carbon
emission, raising severe energy and environmental concerns. Therefore, it is significant and …
emission, raising severe energy and environmental concerns. Therefore, it is significant and …
A gentle introduction to reinforcement learning and its application in different fields
Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …
become one of the most important and useful technology. It is a learning method where a …
Federated reinforcement learning: Linear speedup under markovian sampling
Since reinforcement learning algorithms are notoriously data-intensive, the task of sampling
observations from the environment is usually split across multiple agents. However …
observations from the environment is usually split across multiple agents. However …
An application of deep reinforcement learning to algorithmic trading
This scientific research paper presents an innovative approach based on deep
reinforcement learning (DRL) to solve the algorithmic trading problem of determining the …
reinforcement learning (DRL) to solve the algorithmic trading problem of determining the …
Deep reinforcement learning based adaptive operator selection for evolutionary multi-objective optimization
Evolutionary algorithms (EAs) have become one of the most effective techniques for multi-
objective optimization, where a number of variation operators have been developed to …
objective optimization, where a number of variation operators have been developed to …
A survey of reinforcement learning algorithms for dynamically varying environments
S Padakandla - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
Reinforcement learning (RL) algorithms find applications in inventory control, recommender
systems, vehicular traffic management, cloud computing, and robotics. The real-world …
systems, vehicular traffic management, cloud computing, and robotics. The real-world …
Deep reinforcement learning for band selection in hyperspectral image classification
Band selection refers to the process of choosing the most relevant bands in a hyperspectral
image. By selecting a limited number of optimal bands, we aim at speeding up model …
image. By selecting a limited number of optimal bands, we aim at speeding up model …
[HTML][HTML] Deep reinforcement learning for traffic signal control with consistent state and reward design approach
Abstract Intelligent Transportation Systems are essential due to the increased number of
traffic congestion problems and challenges nowadays. Traffic Signal Control (TSC) plays a …
traffic congestion problems and challenges nowadays. Traffic Signal Control (TSC) plays a …
Disentangling transfer in continual reinforcement learning
The ability of continual learning systems to transfer knowledge from previously seen tasks in
order to maximize performance on new tasks is a significant challenge for the field, limiting …
order to maximize performance on new tasks is a significant challenge for the field, limiting …