Pri-DDQN: learning adaptive traffic signal control strategy through a hybrid agent by Yanliu Zheng, Juan Luo, Han Gao, Yi Zhou, Keqin Li

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Adaptive Security Solutions for NOMA Networks: The Role of DDPG and RIS-Equipped UAVs by Syed Zain Ul Abideen, Abdul Wahid, Mian Muhammad Kamal

Subjects: “…non-orthogonal multiple access, reconfigurable intelligent surface, unmanned aerial vehicles, deep reinforcement learning, deep deterministic policy gradient, physical layer security.…”
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Trust Region Policy Learning for Adaptive Drug Infusion with Communication Networks in Hypertensive Patients by Mai The Vu, Seong Han Kim, Ha Le Nhu Ngoc Thanh, Majid Roohi, Tuan Hai Nguyen

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Multi-Fault-Tolerant Operation of Grid-Interfaced Photovoltaic Inverters Using Twin Delayed Deep Deterministic Policy Gradient Agent by Shyamal S. Chand, Branislav Hredzak, Maurizio Cirrincione

Subjects: “…reinforcement learning…”
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Controlled Signal Technique in VL‐NOMA Communication Under Interference‐Controlled Environment With Intelligent Reflecting Surfaces by C. E. Ngene, Prabhat Thakur, Ghanshyam Singh

Subjects: “…deep reinforcement learning (DRL)…”
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AI/ML Enabled Automation System for Software Defined Disaggregated Open Radio Access Networks: Transforming Telecommunication Business by Sunil Kumar

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Clinical Applications of Machine Learning by Nadayca Mateussi, PhD, Michael P. Rogers, MD, Emily A. Grimsley, MD, Meagan Read, MD, Rajavi Parikh, DO, Ricardo Pietrobon, MD, PhD, Paul C. Kuo, MD

“…This review introduces interpretable predictive machine learning approaches, natural language processing, image recognition, and reinforcement learning methodologies to familiarize end users. …”
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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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Data-driven joint routing, topology, and mobility design for FANET systems using a digital twin approach by Basma M. Mohammad El-Basioni

“…The decision support of the network digital twin is provided by model-based reinforcement learning using dynamic programming and policy iteration algorithm. …”
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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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An adaptive video stream transmission control method for wireless heterogeneous networks based on A3C by Zhiqiang LUO, Wei WANG, Xiaorong ZHU

“…The adaptive bit rate (ABR) algorithm has become the focus research in video transmission.However,due to the characteristics of 5G wireless heterogeneous networks,such as large fluctuation of channel bandwidth and obvious differences between different networks,the adaptive video stream transmission with multi-terminal cooperation was faced with great challenges.An adaptive video stream transmission control method based on deep reinforcement learning was proposed.First of all,a video stream dynamic programming model was established to jointly optimize the transmission rate and diversion strategy.Since the solution of this optimization problem depended on accurate channel estimation,dynamically changing channel state was difficult to achieve.Therefore,the dynamic programming problem was improved to reinforcement learning task,and the A3C algorithm was used to dynamically determine the video bit rate and diversion strategy.Finally,the simulation was carried out according to the measured network data,and compared with the traditional optimization method,the method proposed better improved the user QoE.…”
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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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Fast Frequency Control Strategy Based on Worst-Case Network Attack Perception by Wentao Xu, Zhenghang Song, Peiyuan Guan

“…This strategy focuses on frequency stability in power systems with a high penetration of renewable energy, and utilizes a reinforcement learning agent to intelligently adjust the power setpoint of voltage source converters (VSCs), ensuring that both the frequency and the rate of change of the frequency remain within permissible limits. …”
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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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Motor synergy and energy efficiency emerge in whole-body locomotion learning by Guanda Li, Mitsuhiro Hayashibe

“…We investigated the emergence of synergies through deep reinforcement learning of whole-body locomotion tasks. We carried out a joint-space synergy analysis on whole-body control solutions for walking and running agents in simulated environments. …”
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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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