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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Review of Intelligent Routing Technology in Integrated Satellite-Ground Network by Suzhi CAO, Xue SUN, Houpeng WANG, Jiarong HAN, Siyan PAN, Lei YAN

“…In view of the complex routing problems faced by the integrated satellite-ground network, the research progress of softwaredefi ned integrated satellite-ground network and intelligent routing was investigated.The training and deployment schemes of on-orbit routing mechanisms based on deep reinforcement learning were discussed, and a specifi c analysis was made in conjunction with the space intelligent computing platform.…”
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Review on meta-learning by Yingzhao ZHU, Man LI

“…Deep learning and reinforcement learning are limited by small sample data set, which is impossible to realize the strong generalization learning ability.Meta-learning can make up for their shortcomings effectively.The values formed by accumulated experience feedback the corresponding signals to promote the model to adjust itself.It allows the artificial intelligence to learn to complete complex tasks quickly, which implements true artificial intelligence.Firstly, the basic principles of meta-learning were outlined.Secondly, according to the different forms of meta-knowledge, the research status of various methods was analyzed in depth.Finally, the application potential and the future development trends of meta-learning was discussed .…”
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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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Design and open services of JiuTian intelligent network simulation platform by Lei ZHAO, Miaomiao ZHANG, Guangyu LI, Zhuowen GUAN, Sijia LIU, Zhaobin XIAO, Yuting CAO, Zhe LYU, Yanping LIANG

“…The JiuTian intelligent network simulation platform was proposed, which could provide wireless communication simulation data services for the open innovation platform.The platform contained a series of scalable simulator functionalities, offering open services that enable users to use reinforcement learning algorithms for model training and inference based on simulation environments and data.Additionally, it allowed users to address optimization tasks in different scenarios by uploading and updating parameter configurations.The platform and its open services were primarily introduced from the perspectives of background, overall architecture, simulator, business scenarios, and future directions.…”
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Impaired arbitration between reward-related decision-making strategies in Alcohol Users compared to Alcohol Non-Users: a computational modeling study by Srinivasan A. Ramakrishnan, Riaz B. Shaik, Tamizharasan Kanagamani, Gopi Neppala, Jeffrey Chen, Vincenzo G. Fiore, Christopher J. Hammond, Shankar Srinivasan, Iliyan Ivanov, V. Srinivasa Chakravarthy, Wouter Kool, Muhammad A. Parvaz

“…Abstract Reinforcement learning studies propose that decision-making is guided by a tradeoff between computationally cheaper model-free (habitual) control and costly model-based (goal-directed) control. …”
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A survey on AI techniques applied in the satellite communication/satellite Internet field by Yaqiong LIU, Zhe LYU, Yafei ZHAO, Guochu SHOU

“…The birth of satellite Internet brings new development opportunities, but also many challenges.How artificial intelligence, as an important auxiliary tool, was widely used in the field of satellite communication/satellite Internet in the context of the development of space-air-ground integration, was investigated, which involved communication anti-jamming, communication routing, satellite-terrestrial network system architecture, constellation operation and management and other scenarios.The AI algorithms included traditional machine learning, deep learning, reinforcement learning and so on.Finally, by taking the development trend of the AI applied in the satellite field into consideration, several future research directions were put forward, which provided new ideas and technical solutions for the intelligent development of satellite field in our country.…”
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Algorithm for intelligent collaborative target search and trajectory planning of MAV/UAV by Zhuo LU, Qihui WU, Fuhui ZHOU

“…Based on the manned aerial vehicle (MAV) / unmanned aerial vehicle (UAV) intelligent cooperation platform, the search of multiple interfered signal sources with unknown locations and trajectory planning were studied.Considering the real-time and dynamic nature of the search process, a MAV/UAV intelligent collaborative target search and trajectory planning (MUICTSTP) algorithm based on multi-agent deep reinforcement learning (MADRL) was proposed.Each UAV made online decision on trajectory planning by sensing the received interference signal strength (RISS) values, and then transmitted the sensing information and decision-making actions to the MAV to obtain the global evaluation.The simulation results show that the proposed algorithm exhibits better performance in long-term RISS, collision, and other aspects compared to other algorithms, and the learning strategy is better.…”
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Joint intelligent optimization of task offloading and service caching for vehicular edge computing by Lei LIU, Chen CHEN, Jie FENG, Qingqi PEI, Ci HE, Zhibin DOU

“…Given the contradiction between limited network resources and massive user demands in Internet of vehicles, an intelligent vehicular edge computing network architecture was proposed to achieve the comprehensive cooperation and intelligent management of network resources.Based on this architecture, a joint optimization scheme of task offloading and service caching was furtherly devised, which formulated an optimization problem about how to offload tasks and allocate computation and cache resources.In view of the dynamics, randomness and time variation of vehicular networks, an asynchronous distributed reinforcement learning algorithm was employed to obtain the optimal task offloading and resource management policy.Simulation results demonstrate that the proposed algorithm achieves significant performance improvement in comparison with the other schemes.…”
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Intelligent adaptive edge systems：exploration and open issues by Xu WANG, Nanxi CHEN, Roujia ZHANG

“…Edge intelligence has emerged as a promising trend of the new generation of Internet of things.Edge computing devices are widely distributed, with various diverse end devices and services, delay sensitive, and serve mobile terminals.Therefore, the edge system needs to provide flexible, diverse, reconfigurable and scalable services.From the application fields of adaptive edge computing, the application requirements of intelligent adaptive edge systems were explored, the existing adaptive edge systems and their basic framework were analyzed and summarized, and the application of artificial intelligence technologies was discussed, such as deep learning and reinforcement learning.Then, how to design a special intelligent algorithm in specific application fields was introduced.Finally, the research status and future challenges in this field were discussed.…”
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Dynamic hierarchy resource management for heterogeneous cognitive network by Juan WEN, Min SHENG, Yan ZHANG

“…A dynamic hierarchy resource management approach-DHRM based on intelligent prediction was proposed for heterogeneous cognitive network.In DHRM,according to different time scale,the method of wavelet neural network,wiener prediction and reinforcement learning were brought to get the variation of traffic d ion,the resource requirement of the handover calls,and the information of users’preferences,and available hierarchical resources of all networks were allocated flexibly.Multi-attribute decision making method,based on network status and user preference was used to make decision to dynamically assign network traffic flow to the most appropriate network.Simulation results show that,the system capacity is improved about 20% by DHRM compared with the other joint radio resource management algorithms.…”
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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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Random Access Technology of NTN Network for Low Orbit Satellite by SONG Yanjun, SUN Chenhua, XIE Wenxuan, XU Jun, ZHANG Zhili, ZHANG Jing

“…A new idea of using reinforcement learning method to design access class barring factors was proposed to solve the random access congestion problem in low orbit NTN scenario.…”
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Network media streaming offloading algorithm based on QoE in mobile edge network by Zaijian WANG, Hao CHENG

“…Aiming at the problems of high-latency, high energy consumption, high bandwidth, and poor quality of experience (QoE) caused by emerging network media streaming business in mobile edge computing, a computing offloading algorithm based on QoE feedback configuration was proposed.Firstly, both preprocessing and priority were comprehensively considered to maximize network resource utilization.Meanwhile, different weights were assigned to the computation tasks for establishing a resource allocation relationship.Secondly, after comprehensively taking into account deadline, computing resource, power and bandwidth constraint, an QoE model was established where the optimization objective was the weighted sum of task delay, energy consumption and precision, and the method of Lagrange multipliers was utilized to solve the established model.Simulation results indicate that, compared with the deep reinforcement learning-based online offloading algorithm, the proposed algorithm can effectively optimize the resource allocation and better improve the QoE.…”
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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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Joint optimization strategy of age of information and energy consumption for offloading and scheduling in WBAN by ZHANG Zheng, XIE Xin, BAI Tong, LIN Jinzhao, LI Zhangyong

“…To handle the strong coupling between offloading and scheduling decisions, a two-layer Markov decision process (MDP) was used to approximate the optimal solution. A deep reinforcement learning (DRL) approach was introduced to address the dimensionality issue. …”
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