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DM-DQN: Dueling Munchausen deep Q network for robot path planning
In order to achieve collision-free path planning in complex environment, Munchausen deep Q-learning network (M-DQN) is applied to mobile robot to...
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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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SSPO-DQN spark: shuffled student psychology optimization based deep Q network with spark architecture for big data classification
In information analysis and systematic extraction of complex or huge dataset, big data plays a vital role. The massive growth of large-scale data...
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An Improved Deep Q-Network with Convolution Block Attention
Deep Reinforcement Learning technology combines Reinforcement Learning and Deep Learning, and uses the strong representation ability of Deep Learning...
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Multi-Agent Path Planning Method Based on Improved Deep Q-Network in Dynamic Environments
The multi-agent path planning problem presents significant challenges in dynamic environments, primarily due to the ever-changing positions of...
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Deep Q-Network for AI Soccer
Reinforcement learning has shown an outstanding performance in the applications of games, particularly in Atari games as well as Go. Based on these...
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Deep Q Network-Based Controller for Vertical Takeoff and Landing System
In this study, the reinforcement learning-based controller algorithm design is developed to control the pitch angle of the vertical takeoff and...
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DQN-based resource allocation for NOMA-MEC-aided multi-source data stream
This paper investigates a non-orthogonal multiple access (NOMA)-aided mobile edge computing (MEC) network with multiple sources and one computing...
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Intelligent Robust Disturbance Rejection Control of UAV Based on Deep Q Network
The controller design of unmanned aerial vehicle (UAV) often faces challenges such as significant parameter uncertainty, external interference,...
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Deep Q Network Method for Dynamic Job Shop Scheduling Problem
Nowadays, rule-based heuristic methods for scheduling planning in production environments are commonly used, but their effectiveness is heavily...
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Deep Q-learning with hybrid quantum neural network on solving maze problems
Quantum computing holds great potential for advancing the limitations of machine learning algorithms to handle higher dimensions of data and reduce...
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Study of Q-learning and deep Q-network learning control for a rotary inverted pendulum system
The rotary inverted pendulum system (RIPS) is an underactuated mechanical system with highly nonlinear dynamics and it is difficult to control a RIPS...
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Simulation Results of a DQN Based AAV Testbed in Corner Environment: A Comparison Study for Normal DQN and TLS-DQN
The Deep Q-Network (DQN) is one of the deep reinforcement learning algorithms, which uses deep neural network structure to estimate the Q-value in...
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A New Fault Diagnosis Method Based on Improved DQN for Cutting Tools
The practices of fault diagnosis present challenges in obtaining sensitive fault characteristics of tool system leading to poor fault diagnosis...
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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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Local Planning Strategy Based on Deep Reinforcement Learning Over Estimation Suppression
Local planning is a critical and difficult task for intelligent vehicles in dynamic transportation environments. In this paper, a new method Suppress...
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DQN-based mobile edge computing for smart Internet of vehicle
In this paper, we investigate a multiuser mobile edge computing (MEC)-aided smart Internet of vehicle (IoV) network, where one edge server can help...
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Adaptive scheduling for multi-objective resource allocation through multi-criteria decision-making and deep Q-network in wireless body area networks
To provide compelling trade-offs among conflicting optimization criteria, various scheduling techniques employing multi-objective optimization (MOO)...
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An Energy-Efficient Data Offloading Strategy for 5G-Enabled Vehicular Edge Computing Networks Using Double Deep Q-Network
In the era of fifth-generation (5G)-enabled vehicular edge computing (VEC), efficient data offloading strategies are essential. The complexities...
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Planetary Flight Obstacle Avoidance Guidance Method Based on ES and DQN
Obstacle avoidance constitutes a critical challenge in planetary exploration flights. Traditional approaches struggle to meet safety requirements due...