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Deep reinforcement learning-based adaptive modulation for OFDM underwater acoustic communication system
Due to the time-varying and space-varying characteristics of the underwater acoustic channel, the communication process may be seriously disturbed....
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Sample efficient deep reinforcement learning for Diwata microsatellite reaction wheel attitude control
The Philippines has launched Diwata satellites to undertake different scientific missions. Low-orbit microsatellites are prone to external...
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A novel investigation on the effects of state and reward structure in designing deep reinforcement learning-based controller for nonlinear dynamical systems
In the last decade, the popularity of deep reinforcement learning (DRL)-based controller design for complex and uncertain nonlinear dynamic systems...
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Dynamic multi-objective sequence-wise recommendation framework via deep reinforcement learning
Sequence-wise recommendation, where recommend exercises to each student step by step, is one of the most exciting tasks in the field of intelligent...
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Learning positioning policies for mobile manipulation operations with deep reinforcement learning
This work focuses on the operation of picking an object on a table with a mobile manipulator. We use deep reinforcement learning (DRL) to learn a...
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Transfer Learning in Deep Reinforcement Learning
Reinforcement learning has quickly risen in popularity because of its simple, intuitive nature, and its powerful results. In this paper, we study a...
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Air Combat Agent Construction Based on Hybrid Self-play Deep Reinforcement Learning
In increasingly complex air combat, machine learning method such as deep reinforcement learning (DRL) for air combat decision-making control has...
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Deep reinforcement learning-based framework for constrained any-objective optimization
Optimization problems are widely used in many real-world applications. These problems are rarely unconstrained and are usually considered constrained...
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Dependency-aware task offloading based on deep reinforcement learning in mobile edge computing networks
With the rapid development of innovative applications, lots of computation-intensive and delay-sensitive tasks are emerging. Task offloading, which...
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Navigation of Mobile Robots Based on Deep Reinforcement Learning: Reward Function Optimization and Knowledge Transfer
This paper presents an end-to-end online learning navigation method based on deep reinforcement learning (DRL) for mobile robots, whose objective is...
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Priority-Aware Resource Allocation for RIS-assisted Mobile Edge Computing Networks: A Deep Reinforcement Learning Approach
In this work, we investigate a reconfigurable intelligent surface (RIS) assisted mobile edge computing (MEC) network, where multiple users offload...
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A deep reinforcement approach for computation offloading in MEC dynamic networks
In this study, we investigate the challenges associated with dynamic time slot server selection in mobile edge computing (MEC) systems. This study...
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Deep Reinforcement Learning (DRL)-Based Methods for Serverless Stream Processing Engines: A Vision, Architectural Elements, and Future Directions
Streaming applications are becoming widespread across an extensive range of business domains as an increasing number of sources continuously produce...
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A deep reinforcement learning optimization framework for supercritical airfoil aerodynamic shape design
In the context of traditional aerodynamic shape optimization design methods, the necessity to re-execute the complete optimization process when the...
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Modeling and Deep Reinforcement Learning Based Control Parameter Tuning for Voltage Source Converter in a Renewable Energy Generation System
The fast response and low inertia characteristics of converter-based generation (CBG) lead to a new stability issue that limits renewable energy...
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Solving the Pallet Loading Problem with Deep Reinforcement Learning
Because of its complexity and prominence in the logistic field, the Pallet Loading Problem (PLP) has caught the attention of academics and...
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Cooperative multi-agent target searching: a deep reinforcement learning approach based on parallel hindsight experience replay
Multi-agent multi-target search strategies can be utilized in complex scenarios such as post-disaster search and rescue by unmanned aerial vehicles....
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Unstructured surface mesh smoothing method based on deep reinforcement learning
In numerical simulations such as computational fluid dynamics simulations or finite element analyses, mesh quality affects simulation accuracy...
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Attention-deep reinforcement learning jointly beamforming based on tensor decomposition for RIS-assisted V2X mmWave massive MIMO system
To achieve a green and sustainable wireless communication network, the reconfigurable intelligent surface (RIS) technology has emerged as an emerging...
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Intelligent deep reinforcement learning-based scheduling in relay-based HetNets
We consider a fundamental file dissemination problem in a two-hop relay-based heterogeneous network consisting of a macro base station, a half-duplex...
