SPARQ: Efficient Entanglement Distribution and Routing in Space–Air–Ground Quantum Networks by Mohamed Shaban, Muhammad Ismail, Walid Saad

“…To solve the entanglement routing problem, a deep reinforcement learning (RL) framework is proposed and trained using deep Q-network (DQN) on multiple graphs of SPARQ to account for the network dynamics. …”
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Energy-Efficient Aerial STAR-RIS-Aided Computing Offloading and Content Caching for Wireless Sensor Networks by Xiaoping Yang, Quanzeng Wang, Bin Yang, Xiaofang Cao

“…We propose a deep reinforcement learning (DRL)–successive convex approximation (SCA) combined algorithm to iteratively achieve near-optimal solutions with low complexity. …”
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Sensation seeking and risk adjustment: the role of reward sensitivity in dynamic risky decisions by Yin Qianlan, Chen Shou, Hou Tianya, Dong Wei, Taosheng Liu

“…By integrating the reinforcement learning model and neural measures obtained from dynamic risk-taking tasks, we aim to explore how these personality traits influence individual decision-making processes and engagement in risk-related activities. …”
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Dynamic Service Placement in Edge Computing: A Comparative Evaluation of Nature-Inspired Algorithms by Aqeel H. Kazmi, Alessandro Staffolani, Tianhao Zhang, Christian Cabrera, Siobhan Clarke

“…The study covers nature-inspired approaches, including both meta-heuristics and reinforcement learning. Our experimental findings offer valuable insights into the strengths and weaknesses of the selected nature-inspired algorithms for service placement optimization, evaluated for applications with different QoS requirements. …”
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Quantitative Representation of Autonomous Driving Scenario Difficulty Based on Adversarial Policy Search by Shuo Yang, Caojun Wang, Yuanjian Zhang, Yuming Yin, Yanjun Huang, Shengbo Eben Li, Hong Chen

“…Specifically, the concept of environment agent is proposed, and a reinforcement learning method combined with mechanism knowledge is constructed for policy search to obtain an agent with an adversarial behavior. …”
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Robot Dynamic Path Planning Based on Prioritized Experience Replay and LSTM Network by Hongqi Li, Peisi Zhong, Li Liu, Xiao Wang, Mei Liu, Jie Yuan

“…To address the issues of slow convergence speed, poor dynamic adaptability, and path redundancy in the Double Deep Q Network (DDQN) within complex obstacle environments, this paper proposes an enhanced algorithm within the deep reinforcement learning framework. This algorithm, termed LPDDQN, integrates Prioritized Experience Replay (PER) and the Long Short Term Memory (LSTM) network to improve upon the DDQN algorithm. …”
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Artificial intelligence and assisted reproductive technology: A comprehensive systematic review by Yen-Chen Wu, Emily Chia-Yu Su, Jung-Hsiu Hou, Ching-Jung Lin, Krystal Baysan Lin, Chi-Huang Chen

“…The effectiveness of different machine learning paradigms—supervised, unsupervised, and reinforcement learning—in improving ART-related procedures was particularly examined. …”
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Inverse design of nanophotonic devices enabled by optimization algorithms and deep learning: recent achievements and future prospects by Kim Junhyeong, Kim Jae-Yong, Kim Jungmin, Hyeong Yun, Neseli Berkay, You Jong-Bum, Shim Joonsup, Shin Jonghwa, Park Hyo-Hoon, Kurt Hamza

“…Furthermore, we explore state-of-the-art deep learning techniques, involving discriminative models, generative models, and reinforcement learning. We also introduce and categorize several notable inverse-designed nanophotonic devices and their respective design methodologies. …”
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Depression Detection in Social Media: A Comprehensive Review of Machine Learning and Deep Learning Techniques by Waleed Bin Tahir, Shah Khalid, Sulaiman Almutairi, Mohammed Abohashrh, Sufyan Ali Memon, Jawad Khan

“…While this review highlights advancements in social media-based depression detection, it excludes alternative approaches like graph-based systems and reinforcement learning, and its focus on social media may limit its applicability to other domains.…”
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Multi-Objective Simulated Annealing for Efficient Task Allocation in UAV-Assisted Edge Computing for Smart City Traffic Management by Ahmed Shamil Mustafa, Salman Yussof, Nurul Asyikin Mohamed Radzi

“…While existing technologies provide solutions for data collection (UAVs), processing (computer vision), and control (reinforcement learning), the integration and resource optimization of these components remains a significant challenge. …”
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Adaptive Cut Selection in Mixed-Integer Linear Programming by Turner, Mark, Koch, Thorsten, Serrano, Felipe, Winkler, Michael

“…We present a reinforcement learning framework for selecting cuts, and train our design using said framework over MIPLIB 2017 and a neural network verification data set. …”
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Enhancing patient education on the role of tibial osteotomy in the management of knee osteoarthritis using a customized ChatGPT: a readability and quality assessment by Stephen Fahy, Stephan Oehme, Danko Dan Milinkovic, Benjamin Bartek

“…Two ChatGPT-4 models were compared: a native version and a fine-tuned model (“The Knee Guide”) optimized for readability and source citation through Instruction-Based Fine-Tuning (IBFT) and Reinforcement Learning from Human Feedback (RLHF). The responses were evaluated for quality using the DISCERN criteria and readability using the Flesch Reading Ease Score (FRES) and Flesch-Kincaid Grade Level (FKGL).ResultsThe native ChatGPT-4 model scored a mean DISCERN score of 38.41 (range 25–46), indicating poor quality, while “The Knee Guide” scored 45.9 (range 33–66), indicating moderate quality. …”
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PREDICTING THE LEARNING PATH TO LEARNER’S OPTIMUM COMPREHENSION by Ifeanyi Isaiah Achi, Chukwuemeka Odi Agwu, Christopher Chizoba Nnamene, Sylvester C. Aniobi, Ifebude Barnabas C., Kelechi Christian Oketa, Godson Kenechukwu Ezeh, John Otozi Ugah

“…This research paper adopted Reinforcement Learning and the Markov decision process, specifically the Markov Chain approach, in developing an improved model for prediction. …”
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Nursing management in the fever clinic in a general hospital under the normalization of prevention and control of COVID-19 (新型冠状病毒肺炎疫情常态化防控期间综合医院发热门诊护理管理实践)... by YAN Junjie (闫俊杰), QI Wenjing (綦文婧), LI Jia (李佳), WANG Meiyu (王美玉)

“…Efforts were made in the following areas such as reorganization of fever clinics, improvement and implementation of the nosocomial infection control system, preparation of relevant medical staff, enhancement of training and assessment, reinforcement learning of relevant regulations and documents, implementation of prevention and control measures, and nursing team building. …”
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A Centralized Multi-Agent DRL-Based Trajectory Control Strategy for Unmanned Aerial Vehicle-Enabled Wireless Communications by Getaneh Berie Tarekegn, Rong-Terng Juang, Belayneh Abebe Tesfaw, Hsin-Piao Lin, Huan-Chia Hsu, Robel Berie Tarekegn, Li-Chia Tai

“…The trajectory of the aerial base stations is then continuously adjusted through a centralized multi-agent deep reinforcement learning algorithm to optimize communication performance. …”
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Time Perception Test in IntelliCage System for Preclinical Study: Linking Depression and Serotonergic Modulation by Olga Sysoeva, Rauf Akhmirov, Maria Zaichenko, Ivan Lazarenko, Anastasiya Rebik, Nadezhda Broshevitskaja, Inna Midzyanovskaya, Kirill Smirnov

“…Disturbances in time perception are also reported in depression with one of the behavioral schedules used to study interval timing, differential-reinforcement-learning-of-low-rate, having been shown to have high predictive validity for an antidepressant effect. …”
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Advances in machine learning applications to resource technology for organic solid waste by Hongzhi MA, Yichan LIU, Jihua ZHAO, Fan FEI, Ming GAO, Qunhui WANG

“…Another strategy is the use of reinforcement learning and transfer learning, which effectively address dynamic environments and small datasets, respectively. …”
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Research Progress and Prospect of Multi-robot Collaborative SLAM in Complex Agricultural Scenarios by MA Nan, CAO Shanshan, BAI Tao, KONG Fantao, SUN Wei

“…Secondly, the combination of deep learning and reinforcement learning techniques is expected to empower robots to better interpret environmental patterns, adapt to dynamic changes, and make more effective real-time decisions. …”
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The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance by Hazrat Bilal, Muhammad Nadeem Khan, Sabir Khan, Muhammad Shafiq, Wenjie Fang, Rahat Ullah Khan, Mujeeb Ur Rahman, Xiaohui Li, Qiao-Li Lv, Bin Xu

“…Supervised learning, unsupervised learning, deep learning, reinforcement learning, and natural language processing are some of the main tools used in this domain. …”
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