A Deep Q-Learning Algorithm With Guaranteed Convergence for Distributed and Uncoordinated Operation of Cognitive Radios by Ankita Tondwalkar, Andres Kwasinski

“…This paper studies a deep reinforcement learning technique for distributed resource allocation among cognitive radios operating under an underlay dynamic spectrum access paradigm which does not require coordination between agents during learning. …”
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Construction of Personalized Learning Platform Based on Intelligent Algorithm in the Context of Industry Education Integration by Zhifang Qian

“…Based on the concept of integration of production and education, with the help of intelligent recommendation algorithm of reinforcement learning, a personalized learning platform based on intelligent algorithm is constructed. …”
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Intelligent Buses in a Loop Service: Emergence of No-Boarding and Holding Strategies by Vee-Liem Saw, Luca Vismara, Lock Yue Chew

“…We study how N intelligent buses serving a loop of M bus stops learn a no-boarding strategy and a holding strategy by reinforcement learning. The no-boarding and holding strategies emerge from the actions of stay or leave when a bus is at a bus stop and everyone who wishes to alight has done so. …”
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A Neural Correlate of Predicted and Actual Reward-Value Information in Monkey Pedunculopontine Tegmental and Dorsal Raphe Nucleus during Saccade Tasks by Ken-ichi Okada, Kae Nakamura, Yasushi Kobayashi

“…Dopamine, acetylcholine, and serotonin, the main modulators of the central nervous system, have been proposed to play important roles in the execution of movement, control of several forms of attentional behavior, and reinforcement learning. While the response pattern of midbrain dopaminergic neurons and its specific role in reinforcement learning have been revealed, the role of the other neuromodulators remains rather elusive. …”
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Intelligent interference decision algorithm with prior knowledge embedded LSTM-PPO model by ZHANG Jingke, YANG Kai, LI Chao, WANG Hongyan

“…Focusing on the issues of low efficiency and effectiveness in decision-making as well as the instability of traditional reinforcement learning model-based multi-function radar (MFR) jamming decision algorithms, a prior knowledge embedded long short-term memory (LSTM) network-proximal policy optimization (PPO) model based intelligent interference decision algorithm was developed. …”
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Multimedia Tasks-Oriented Edge Computing Offloading Scheme Based on Graph Neural Network in Vehicular Networks by Yong Huang

“…Second, deep Reinforcement Learning (DRL) is introduced to learn optimal task deployment policies, enhancing the efficiency and accuracy of task deployment. …”
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Perbandingan Metode Penyelesaian Permasalahan Optimasi Lintas Domain dengan Pendekatan Hyper-Heuristic Menggunakan Algoritma Reinforcement-Late Acceptance by Anang Firdaus, Ahmad Muklason, Vicha Azthanty Supoyo

“…Dalam meningkatkan kinerja, penelitian ini menguji pengaruh dari adaptasi algoritma Reinforcement Learning (RL) sebagai strategi seleksi LLH dikombinasikan dengan algoritma Late Acceptance sebagai move acceptance, selanjutnya disebut algoritma Reinforcement Learning-Late acceptance (RL-LA). …”
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Deep Learning in Music Generation: A Comprehensive Investigation of Models, Challenges and Future Directions by Kong Xiangchen

“…This review explores various and recent deep learning models, such as Long Short-Term Memory (LSTM) networks, Transformer-based models, Reinforcement Learning (RL), and Diffusion-based architectures, and how they are applied to music generation. …”
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Differential Grey Wolf Load-Balanced Stochastic Bellman Deep Reinforced Resource Allocation in Fog Environment by S. V. Nethaji, M. Chidambaram

“…In a Stochastic Gradient and Deep Reinforcement Learning-based Resource Allocation Model, a stochastic gradient bellman optimality function and Deep Reinforcement Learning are integrated for optimal resource allocation. …”
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Social context affects sequence modification learning in birdsong by Lioba Fortkord, Lena Veit

“…Recently, introducing specific learned modifications into adult song by experimenter-controlled reinforcement learning has emerged as a key protocol to study aspects of vocal learning in songbirds. …”
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Power Quality Assessment and Optimization in FUZZY-Driven Healthcare Devices by Dinesh Kumar Nishad, Saifullah Khalid, Rashmi Singh

“…The CNN-based detection achieves 97% accuracy in classifying events, while the LSTM enables 95% accurate prediction of emerging issues. The reinforcement learning controller achieves 50% faster voltage sag restoration, 20% greater harmonic reduction, and 30% faster critical load recovery during outages compared to conventional methods. …”
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RISKS REDUCING THROUGH INTELLIGENT HEADLIGHT MANAGEMENT: OPTIMIZING Q-LEARNING FOR ELECTRIC VEHICLES by Pitchaya Jamjuntr, Chanchai Techawatcharapaikul, Pannee Suanpang

“…Ultimately, further exploration into the utilization of reinforcement learning for complex optimization issues across various domains is recommended. …”
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Optimized Path Planning and Scheduling in Robotic Mobile Fulfillment Systems Using Ant Colony Optimization and Streamlit Visualization by Isam Sadeq Rasham

“…Methodology: This research introduces a new optimization model for RMFS selection which integrates reinforcement learning with Ant Colony Optimization (ACO). …”
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Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications by Teguh Indra Bayu, Yung-Fa Huang, Jeang-Kuo Chen, Cheng-Hsiung Hsieh, Budhi Kristianto, Erwien Christianto, Suharyadi Suharyadi

“…This innovative method combines a Sugeno-type fuzzy inference system (FIS) control system with a Q-learning reinforcement learning algorithm to optimize the PRR as the key metric for overall network performance. …”
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Interactive English Online Teaching System Based on B/S Model by Wanhong Gu, Yaxiong Yuan

“…Moreover, this paper introduces the strategy gradient of reinforcement learning to the generator of the generation confrontation network to solve the problem that the generation confrontation network is difficult to be used for dialogue generation. …”
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Fast Transfer Navigation for Autonomous Robots by Chen Wang, Xudong Li, Xiaolin Tao, Kai Ling, Quhui Liu, Gan Tao

“…This paper proposes a transfer navigation algorithm and improves its generalization by leveraging deep reinforcement learning and a self-attention module. To simulate the unfurnished indoor environment, we build the virtual indoor navigation (VIN) environment to compare our model and its competitors. …”
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Multilayer Deep Deterministic Policy Gradient for Static Safety and Stability Analysis of Novel Power Systems by Yun Long, Youfei Lu, Hongwei Zhao, Renbo Wu, Tao Bao, Jun Liu

“…The proposed method is stacked with multilayer deep reinforcement learning methods that can be continuously updated online. …”
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Dynamic Car-Following Model With Jerk Suppression for Highway Autonomous Driving by Ke Liu, Jing Ma, Edmund M.-K. Lai

“…This study proposes a dynamic safe car-following strategy that is based on dynamic adjustment of headway time with jerk suppression. Reinforcement learning models trained with this strategy result in enhanced safety and driving comfort, validated using real driving data from the Next Generation Simulation (NGSIM) I-80 and HighD datasets. …”
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A New Early Rumor Detection Model Based on BiGRU Neural Network by Xiangning Chen, Caiyun Wang, Dong Li, Xuemei Sun

“…Specifically, in this model, the input data is firstly refined through account filtering and data standardization, then the BiGRU is used to consider the context relationship, and a reinforcement learning algorithm is applied to detection. …”
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A Strong Maneuvering Target-Tracking Filtering Based on Intelligent Algorithm by Jing Li, Xinru Liang, Shengzhi Yuan, Haiyan Li, Changsheng Gao

“…The algorithm can map the selection of the model set to the selection of the action label and realize the purpose of a deep reinforcement-learning agent to replace the model switching in the traditional VSMM algorithm by reasonably designing a reward function, state space, and network structure. …”
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