A computing allocation strategy for Internet of things’ resources based on edge computing by Zengrong Zhang

“…In order to meet the demand for efficient computing services in big data scenarios, a cloud edge collaborative computing allocation strategy based on deep reinforcement learning by combining the powerful computing capabilities of cloud is proposed. …”
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Convolutional neural network (CNN) configuration using a learning automaton model for neonatal brain image segmentation. by Iran Sarafraz, Hamed Agahi, Azar Mahmoodzadeh

“…., size, length, and width of the filter in each layer along with the type of pooling functions with a reinforcement learning approach and an LA model are determined. …”
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Data-Driven Robust Optimization of the Vehicle Routing Problem with Uncertain Customers by Jingling Zhang, Yusu Sun, Qinbing Feng, Yanwei Zhao, Zheng Wang

“…The Q-learning algorithm in reinforcement learning is introduced into the high-level selection strategy using the hyper-heuristic algorithm, and a hyper-heuristic algorithm based on the Q-learning algorithm is designed to solve the problem. …”
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Bending obstacles when moving a mobile robot by A. V. Sidorenko, N. A. Saladukha

“…The developed software includes the Mobile Robotics Simulation Toolbox, Reinforcement Learning Toolbox, and the Gazebo visualization package for environment simulation. …”
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Environmental regulation, market power and low-carbon development of China's coal power industry chain —Based on both strategy and return perspectives by Jiaming Gao, Li Zhang

“…Furthermore, a reinforcement learning model has been developed using the payoff matrix. …”
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The Ecosystem of AI-Driven Robotics in Pediatric Neurorehabilitation by Tole Sutikno, Lina Handayani

“…We address this review to facilitate an in-depth analysis of the effective integration of advanced technologies, such as artificial emotional intelligence and interactive reinforcement learning, into rehabilitation practices. By critically assessing each element, from the psychological dynamics of patient engagement to the technical intricacies of real-time adaptive learning systems, we can better understand their pivotal roles in enhancing therapeutic efficacy. …”
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Q-Learning-Driven Butterfly Optimization Algorithm for Green Vehicle Routing Problem Considering Customer Preference by Weiping Meng, Yang He, Yongquan Zhou

“…This paper proposes a Q-learning-driven butterfly optimization algorithm (QLBOA) by integrating the Q-learning mechanism of reinforcement learning into the butterfly optimization algorithm (BOA). …”
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Research on Flexible Resource Dynamic Interactive Regulation Technology for Microgrids with High Permeable New Energy by Songsong Chen, Lutao Zhang, Ying Zhou, Ke Chen, Zhongdong Wang, Lihui Xie

“…The first layer adopts the IQ (λ) strategy, which avoids the overestimation and underestimation problems of traditional reinforcement learning by the coupled estimation method. The second layer adopts the HDQC allocation strategy, which solves the problem of low utilization of new energy in the proportional allocation method and improves the adaptability of the regulation strategy in the complex stochastic environment. …”
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Recurrent neural networks with transient trajectory explain working memory encoding mechanisms by Chenghao Liu, Shuncheng Jia, Hongxing Liu, Xuanle Zhao, Chengyu T. Li, Bo Xu, Tielin Zhang

“…Besides activity patterns resembling animal recordings and retained versatility to variable encoding time, TRNNs show better performance in delayed choice and spatial memory reinforcement learning tasks. Therefore, this study provides evidence supporting the transient activity theory to explain the WM mechanism from the model designing point of view.…”
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Artificial intelligence techniques applications in the wastewater: A comprehensive review by Zakur Yahya, Márquez Fausto, Al-Taie Ali, Alsaidi Saif, Alsadoon Abeer, Mirashrafi Seyed Bagher, Flaih Laith, Zakoor Yousif

“…The critical gaps and the future directions in the (AI) algorithms for the wastewater treatment, including the explain ability of the data-driven models or transfer Learning processes and reinforcement learning, are also addressed.…”
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Foot trajectory as a key factor for diverse gait patterns in quadruped robot locomotion by Shura Suzuki, Kosuke Matayoshi, Mitsuhiro Hayashibe, Dai Owaki

“…While substantial progress in robotic mobility has been achieved using reinforcement learning techniques, quadruped animals exhibit superior agility by employing fundamentally different strategies. …”
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Intentionally-underestimated value function at terminal state for temporal-difference learning with mis-designed reward by Taisuke Kobayashi

“…Robot control using reinforcement learning has become popular, but its learning process often terminates midway through an episode for safety and time-saving reasons. …”
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The Role of Cognitive Science and Big Data Technology in the Design of Business Information Management Systems by Yangfan Li

“…Based on the real-time computing technology of massive data, the label optimization scheme of collaborative filtering and reinforcement learning is used to realize the logistics distribution recommendation model and to solve the accuracy and real-time problems of logistics service distribution analysis.…”
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A Deep Q-Network-Based Collaborative Control Research for Smart Ammunition Formation by Jian Shen, Benkang Zhang, Qingyu Zhu, Pengyun Chen

“…Next, we describe the SAF collaborative control process as a Markov decision process (MDP) with the application of the reinforcement learning (RL) technique. Then, the basic framework ε-imitation action-selecting strategy and the algorithm details were developed to address the SAF control problem based on the DQN scheme. …”
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LazyAct: Lazy actor with dynamic state skip based on constrained MDP. by Hongjie Zhang, Zhenyu Chen, Hourui Deng, Chaosheng Feng

“…Deep reinforcement learning has achieved significant success in complex decision-making tasks. …”
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Multiple Unmanned Aerial Vehicle Collaborative Target Search by DRL: A DQN-Based Multi-Agent Partially Observable Method by Heng Xu, Dayong Zhu

“…In unknown environments, UAVs can significantly reduce the risk of casualties and improve the safety and covertness when performing missions. Reinforcement Learning allows agents to learn optimal policies through trials in the environment, enabling UAVs to respond autonomously according to the real-time conditions. …”
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Using EEG technology to enhance performance measurement in physical education by Zhaofeng Zhai, Zhaofeng Zhai, Lu Han, Wei Zhang

“…APEO combines biomechanical modeling, engagement prediction through recurrent neural networks, and reinforcement learning to tailor physical activity interventions. …”
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Human-Inspired Gait and Jumping Motion Generation for Bipedal Robots Using Model Predictive Control by Zhen Xu, Jianan Xie, Kenji Hashimoto

“…Currently, methods based on hybrid zero dynamics and reinforcement learning have been employed to enhance the walking and hopping capabilities of humanoid robots; however, model predictive control (MPC) presents two significant advantages: it can adapt to more complex task requirements and environmental conditions, and it allows for various walking and hopping patterns without extensive training and redesign. …”
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Smart City Traffic Flow and Signal Optimization Using STGCN-LSTM and PPO Algorithms by Tuxiang Lin, Rongliang Lin

“…Future research will explore edge computing, multi-agent reinforcement learning, and real-time data integration to further enhance scalability and adaptability.…”
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Customer segmentation in the digital marketing using a Q-learning based differential evolution algorithm integrated with K-means clustering. by Guanqun Wang

“…This paper proposes a customer segmentation framework within the realm of digital marketing, which integrates a reinforcement learning-based differential evolution algorithm with K-means clustering using dimensionality reduction techniques to address challenges in the customer segmentation process. …”
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