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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Enabling Fast AI-Driven Inverse Design of a Multifunctional Nanosurface by Parallel Evolution Strategies by Ashish Chapagain, Dima Abuoliem, In Ho Cho

“…As a successful alternative to reinforcement learning, ES performed well for the AI-driven inverse design. …”
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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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Machine learning for QoS and security enhancement of RPL in IoT-Enabled wireless sensors by Abubakar Wakili, Sara Bakkali, Ahmed El Hilali Alaoui

“…Our approach integrates a random forest model for precise traffic classification, a reinforcement learning module for dynamic and adaptive routing, and a modified RPL objective function that incorporates classification outcomes into routing decisions. …”
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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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A Multi-Agent Control Architecture for a Robotic Wheelchair by C. Galindo, A. Cruz-Martin, J. L. Blanco, J. A. Fernńndez-Madrigal, J. Gonzalez

“…Within our design, agents have their own intentions and beliefs, have different abilities (that include algorithmic behaviours and human skills) and also learn autonomously the most convenient method to carry out their actions through reinforcement learning. The proposed architecture is illustrated with a real assistant robot: a robotic wheelchair that provides mobility to impaired or elderly people.…”
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Distributionally Robust Policy and Lyapunov-Certificate Learning by Kehan Long, Jorge Cortes, Nikolay Atanasov

“…To demonstrate the efficacy and efficiency of the proposed methodology, we compare it with an uncertainty-agnostic baseline approach and several reinforcement learning approaches in two control problems in simulation. …”
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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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Loss Architecture Search for Few-Shot Object Recognition by Jun Yue, Zelang Miao, Yueguang He, Nianchun Du

“…This procedure is repeated and implemented in the reinforcement learning framework for finding the best loss architecture such that the embedding network yields the highest validation accuracy. …”
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Open challenges and opportunities in federated foundation models towards biomedical healthcare by Xingyu Li, Lu Peng, Yu-Ping Wang, Weihua Zhang

“…Foundation models such as ChatGPT, LLaMa, and CLIP, which are trained on vast datasets through methods including unsupervised pretraining, self-supervised learning, instructed fine-tuning, and reinforcement learning from human feedback, represent significant advancements in machine learning. …”
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Optimization of the Rapid Design System for Arts and Crafts Based on Big Data and 3D Technology by Haihan Zhou

“…In the system design, the overall structure design, database design, and functional module design of the system are comprehensively elaborated, and the key issues such as 3D display and home layout generation algorithm based on reinforcement learning are analyzed and designed. In the implementation part of the system, the overall construction of the system and the composition of functional modules are introduced in detail and the main functional modules of the system are presented with interface diagrams. …”
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