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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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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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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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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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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...
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Boosting in-transit entertainment: deep reinforcement learning for intelligent multimedia caching in bus networks
Multimedia content delivery in advanced networks faces exponential growth in data volumes, rendering existing solutions obsolete. This research...
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A path planning method based on deep reinforcement learning for crowd evacuation
Deep reinforcement learning (DRL) is suitable for solving complex path-planning problems due to its excellent ability to make continuous decisions in...
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Hierarchical Goal-Guided Learning for the Evasive Maneuver of Fixed-Wing UAVs based on Deep Reinforcement Learning
Fixed-wing unmanned aerial vehicles (UAVs) will play a vital role in forthcoming military conflicts. Effectively avoiding threats and improving the...
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A deep reinforcement learning-based D2D spectrum allocation underlaying a cellular network
We develop a deep reinforcement learning-based (DRL) spectrum access scheme for device-to-device communications in an underlay cellular network....
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MADDPGViz: a visual analytics approach to understand multi-agent deep reinforcement learning
Deep reinforcement learning (DRL) has received widespread attention recently, where the control policies are trained through deep neural networks....
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Optimizing deep reinforcement learning in data-scarce domains: a cross-domain evaluation of double DQN and dueling DQN
The challenge of limited labeled data is a persistent concern across diverse domains, including healthcare, niche agricultural practices, astronomy...
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Deep Reinforcement Learning Processor Design for Mobile Applications
This book discusses the acceleration of deep reinforcement learning (DRL), which may be the next step in the burst success of artificial intelligence...
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Solving Inventory Management Problems through Deep Reinforcement Learning
Inventory management (e.g. lost sales) is a central problem in supply chain management. Lost sales inventory systems with lead times and complex cost...
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Deep Reinforcement Learning with Inverse Jacobian based Model-Free Path Planning for Deburring in Complex Industrial Environment
In this study, we present an innovative approach to robotic deburring path planning by combining deep reinforcement learning (DRL) with an inverse...
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Fuel-Saving Control Strategy for Fuel Vehicles with Deep Reinforcement Learning and Computer Vision
This study uses deep reinforcement learning (DRL) combined with computer vision technology to investigate vehicle fuel economy. In a driving cycle...
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Multi-objective Deep Reinforcement Learning Based Joint Beamforming and Power Allocation in UAV Assisted Cellular Communication
In order to provide spectrum and energy efficient communication for unmanned aerial vehicle assisted cellular network, the problem of joint...
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Optimization of Fed-Batch Baker’s Yeast Fermentation Using Deep Reinforcement Learning
Fermentation is widely used in chemical industries to produce valuable products. It consumes less energy and has a lesser environmental impact...
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An Efficient Deep Reinforcement Learning Algorithm for Mapless Navigation with Gap-Guided Switching Strategy
Deep reinforcement learning (DRL) has recently received a lot of attention due to its better performance compared to traditional algorithms in...
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Fluid dynamic control and optimization using deep reinforcement learning
This paper presents a review of recent research on applying deep reinforcement learning in fluid dynamics. Reinforcement learning is a technique in...
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Backdoor attacks against deep reinforcement learning based traffic signal control systems
To improve the efficiency of the traffic signal control and alleviate traffic congestion, many researchers focus on applying deep reinforcement...
