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A novel optimization perspective to the problem of designing sequences of tasks in a reinforcement learning framework
Training agents over sequences of tasks is often employed in deep reinforcement learning to let the agents progress more quickly towards better...
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Multi-Agent Natural Actor-Critic Reinforcement Learning Algorithms
Multi-agent actor-critic algorithms are an important part of the Reinforcement Learning (RL) paradigm. We propose three fully decentralized...
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Relational Graph Attention-Based Deep Reinforcement Learning: An Application to Flexible Job Shop Scheduling with Sequence-Dependent Setup Times
This paper tackles a manufacturing scheduling problem using an Edge Guided Relational Graph Attention-based Deep Reinforcement Learning approach....
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Solving Fredholm Integral Equations Using Deep Learning
The aim of this paper is to provide a deep learning based method that can solve high-dimensional Fredholm integral equations. A deep residual neural...
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Optimal control by deep learning techniques and its applications on epidemic models
We represent the optimal control functions by neural networks and solve optimal control problems by deep learning techniques. Adjoint sensitivity...
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Generating a Graph Colouring Heuristic with Deep Q-Learning and Graph Neural Networks
The graph colouring problem consists of assigning labels, or colours, to the vertices of a graph such that no two adjacent vertices share the same...
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Fluids and Deep Learning: A Brief Review
This chapter surveys deep learning models for fluid simulation and rendering. Regarding the former, we can group the works into small and large-scale...
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Improving the efficiency of reinforcement learning for a spacecraft powered descent with Q-learning
Reinforcement learning entails many intuitive and useful approaches to solving various problems. Its main premise is to learn how to complete tasks...
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Unified reinforcement Q-learning for mean field game and control problems
We present a Reinforcement Learning (RL) algorithm to solve infinite horizon asymptotic Mean Field Game (MFG) and Mean Field Control (MFC) problems....
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A Reinforcement Learning Based Slope Limiter for Two-Dimensional Finite Volume Schemes
LimitersKeim, Jens inSchwarz, Anna second-order finiteChiocchetti, Simone volumeRohde, Christian schemesBeck, Andrea are generally too dissipative in...
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Dynamic Police Patrol Scheduling with Multi-Agent Reinforcement Learning
Effective police patrol scheduling is essential in projecting police presence and ensuring readiness in responding to unexpected events in urban...
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Comparison of Backstep** with Reinforcement Learning
A reinforcement learning controller, with training performed on an ARZ PDE model, is developed and evaluated for the amount of training necessary,...
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Recognition of Emotion Behind Speech Using Deep Learning RESNET Algorithm
This work aims to develop a reliable and efficient model that utilizes machine learning techniques to classify speech with high accuracy. The model...
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Learning to select the recombination operator for derivative-free optimization
Extensive studies on selecting recombination operators adaptively, namely, adaptive operator selection (AOS), during the search process of an...
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Multimodal Deep Learning for Manufacturing Systems: Recent Progress and Future Trends
The development of sensing technology provides large amounts and various types of data (e.g., profile, image, point cloud) to describe each stage of...
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A Survey on Deep Learning-Based Diffeomorphic Map**
Diffeomorphic map** is a specific type of registration methods that can be used to align biomedical structures for subsequent analyses....
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Comparison of Reinforcement Learning Based Control Algorithms for One Autonomous Driving Problem
Autonomous driving systems include modules of several levels. Thanks to deep learning architectures at the moment technologies in most of the levels...
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Using Reinforcement Learning for Optimizing COVID-19 Vaccine Distribution Strategies
The COVID-19 pandemic has highlighted the critical importance of efficient and effective vaccine distribution in responding to global health...
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Generative deep learning for decision making in gas networks
A decision support system relies on frequent re-solving of similar problem instances. While the general structure remains the same in corresponding...
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Goal-Oriented Sensitivity Analysis of Hyperparameters in Deep Learning
Tackling new machine learning problems with neural networks always means optimizing numerous hyperparameters that define their structure and strongly...
