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DVNE-DRL: dynamic virtual network embedding algorithm based on deep reinforcement learning
Virtual network embedding (VNE), as the key challenge of network resource management technology, lies in the contradiction between online embedding...
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DRL-HIFA: a dynamic recommendation system with deep reinforcement learning based Hidden Markov Weight Updation and factor analysis
Recommendation Systems have obtained huge attention with notion to assist users in determining their interests by prognosticating their ratings or...
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Matyas–Meyer Oseas based device profiling for anomaly detection via deep reinforcement learning (MMODPAD-DRL) in zero trust security network
The exposure of zero trust security in the Industrial Internet of Things (IIoT) increased in importance in the era where there is a huge risk of...
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Deep reinforcement learning in mobile robotics – a concise review
Mobile robotics is one of the emerging research area in the robotics. The recently evolving techniques, artificial intelligence and precise hardware...
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Deep reinforcement learning-based scheduling in distributed systems: a critical review
Many fields of research use parallelized and distributed computing environments, including astronomy, earth science, and bioinformatics. Due to an...
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An experimental evaluation of deep reinforcement learning algorithms for HVAC control
Heating, ventilation, and air conditioning (HVAC) systems are a major driver of energy consumption in commercial and residential buildings. Recent...
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Deep Reinforcement Learning Model for Stock Portfolio Management Based on Data Fusion
Deep reinforcement learning (DRL) can be used to extract deep features that can be incorporated into reinforcement learning systems to enable...
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Model inductive bias enhanced deep reinforcement learning for robot navigation in crowded environments
Navigating mobile robots in crowded environments poses a significant challenge and is essential for the coexistence of robots and humans in future...
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Deep reinforcement learning imbalanced credit risk of SMEs in supply chain finance
It is crucial to predict the credit risk of small and medium-sized enterprises (SMEs) accurately for the success of supply chain finance (SCF)....
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An efficient intrusive deep reinforcement learning framework for OpenFOAM
Recent advancements in artificial intelligence and deep learning offer tremendous opportunities to tackle high-dimensional and challenging problems....
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Fully dynamic reorder policies with deep reinforcement learning for multi-echelon inventory management
The operation of inventory systems plays an important role in the success of manufacturing companies, making it a highly relevant domain for...
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Deep reinforcement learning for microstructural optimisation of silica aerogels
Silica aerogels are being extensively studied for aerospace and transportation applications due to their diverse multifunctional properties. While...
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Efficient learning of power grid voltage control strategies via model-based deep reinforcement learning
This article proposes a model-based deep reinforcement learning (DRL) method to design emergency control strategies for short-term voltage stability...
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Common challenges of deep reinforcement learning applications development: an empirical study
Machine Learning (ML) is increasingly being adopted in different industries. Deep Reinforcement Learning (DRL) is a subdomain of ML used to produce...
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An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems
Deep Reinforcement Learning (DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with...
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Evaluating the impact of reinforcement learning on automatic deep brain stimulation planning
PurposeTraditional techniques for automating the planning of brain electrode placement based on multi-objective optimization involving many...
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Deep Reinforcement Learning
Similar to humans, RL agents use interactive learning to successfully obtain satisfactory decision strategies. However, in many cases, it is...
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Deep reinforcement learning with positional context for intraday trading
Deep reinforcement learning (DRL) is a well-suited approach to financial decision-making, where an agent makes decisions based on its trading...
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Combining graph neural network with deep reinforcement learning for resource allocation in computing force networks
Fueled by the explosive growth of ultra-low-latency and real-time applications with specific computing and network performance requirements, the...
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Deep Reinforcement Learning with Heuristic Corrections for UGV Navigation
Mapless navigation for mobile Unmanned Ground Vehicles (UGVs) using Deep Reinforcement Learning (DRL) has attracted significantly rising attention in...