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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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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Artificial Intelligence as a Catalyst for Management System Adaptability, Agility and Resilience: Mapping the Research Agenda by Ion Popa, Simona Cătălina Ștefan, Andrei Josan, Corina-Elena Mircioiu, Nicoleta Căruceru

“…Likewise, its thematic and strategic evolution is characterized as a surprising one, managing to incorporate and relate concepts with a strong technical and IT character such as feature extraction, machine learning, reinforcement learning with concepts of a managerial nature as supporting customer-tailored interaction, employee skills development, company productivity, and innovation.…”
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Optimizing Spectrum Trading in Cognitive Mesh Network Using Machine Learning by Ayoub Alsarhan, Anjali Agarwal

“…These complex contradicting objectives are embedded in our reinforcement learning (RL) model that is developed and implemented as shown in this paper. …”
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Agent-Based Modeling and Simulation for the Bus-Corridor Problem in a Many-to-One Mass Transit System by Qinmu Xie, Shoufeng Ma, Ning Jia, Yang Gao

“…By using multiagent modeling and the Bush-Mosteller reinforcement learning model, we simulated the day-to-day evolution of commuters’ departure time choice on a many-to-one mass transit system during the morning peak period. …”
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Parameterless-Growing-SOM and Its Application to a Voice Instruction Learning System by Takashi Kuremoto, Takahito Komoto, Kunikazu Kobayashi, Masanao Obayashi

“…The improved SOM is applied to construct a voice instruction learning system for partner robots adopting a simple reinforcement learning algorithm. User's instructions of voices are classified by the PL-G-SOM at first, then robots choose an expected action according to a stochastic policy. …”
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Modeling and Optimization of Multiaction Dynamic Dispatching Problem for Shared Autonomous Electric Vehicles by Ning Wang, Jiahui Guo

“…Results show that (1) the Kuhn–Munkres algorithm ensures the computational effectiveness in the large-scale real-time application of the AMoD system; (2) the second optimization model considering long-term return can decrease average user waiting time and achieve a 2.78% increase in total revenue compared with the first model; (3) and integrating combinatorial optimization theory with reinforcement learning theory is a perfect package for solving the multiaction dynamic dispatching problem of SAEVs.…”
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Bridging theory and practice in peer-to-peer energy trading: market mechanisms and technological innovations by Pravesh Raghoo, Kalim Shah

“…As such, three market designs are discussed: centralized, decentralized, and distributed, and four pricing mechanisms, which are optimization, game theory, auction-based, and reinforcement learning. Enabling technologies discussed are Energy Internet, Internet of Things, Artificial intelligence, Blockchain, Communication networks, and battery flexibility. …”
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Minimizing Delay and Power Consumption at the Edge by Erol Gelenbe

“…Prior work has mainly focused on two methodologies: (i) formulating non-linear optimizations that lead to NP-hard problems, which are processed via heuristics, and (ii) using AI-based formulations, such as reinforcement learning, that are then tested with simulations. …”
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An energy management strategy for integrated electricity-thermal energy systems using the DQN-CE algorithm by ZHU Jiejie, PI Zhiyong, CHEN Daicai, TAN Hong

“…To address the uncertainty and intermittency of renewable energy output in integrated electricity-thermal energy systems， a reinforcement learning method for energy management is proposed， aiming to minimize the operating costs of the system. …”
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Non-linear multi-objective optimization model of production planning based on fuzzy logic and machine learning by Zahra Saeidi Mobarakeh, Hossein Amoozadkhalili

“…This fuzzy logic is combined with machine learning algorithms such as neural networks and reinforcement learning to create an intelligent and flexible model that effectively adapts to sudden changes in dynamic environments. …”
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Multiple-Camera Patient Tracking Method Based on Motion-Group Parameter Reconstruction by Simeon Karpuzov, George Petkov, Stiliyan Kalitzin

“…This is achieved by automated reinforcement learning and simultaneously applying the interdependences between the cameras. …”
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Enhancing lane detection in autonomous vehicles with multi-armed bandit ensemble learning by J. Arun Pandian, Ramkumar Thirunavukarasu, L. Thanga Mariappan

“…The proposed technique optimizes the segmentation accuracy and treats the attained accuracy as a reward signal in the context of reinforcement learning by interacting with the environment through CNN model selection. …”
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Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities by Izabela Rojek, Dariusz Mikołajewski, Krzysztof Galas, Adrianna Piszcz

“…These algorithms analyze real-time data from sensors and IoT devices to predict energy demand, enabling dynamic load balancing and reducing waste. Reinforcement learning models optimize power distribution by learning from historical patterns and adapting to changes in energy usage in real time. …”
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Delta opioid receptors affect acoustic features of song during vocal learning in zebra finches by Utkarsha A. Singh, Soumya Iyengar

“…We wanted to study if they were also involved in naturally-occurring reinforcement learning behaviors such as vocal learning, using the zebra finch model system. …”
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Composition of Web Services Using Markov Decision Processes and Dynamic Programming by Víctor Uc-Cetina, Francisco Moo-Mena, Rafael Hernandez-Ucan

“…Finally, a comparison with two popular reinforcement learning algorithms, sarsa and Q-learning, shows that these algorithms require one or two orders of magnitude and more time than policy iteration, iterative policy evaluation, and value iteration to handle WSC problems of the same complexity.…”
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Sliding Mode Control for Variable-Speed Trajectory Tracking of Underactuated Vessels with TD3 Algorithm Optimization by Shiya Zhu, Gang Zhang, Qin Wang, Zhengyu Li

“…An adaptive sliding mode controller (SMC) design with a reinforcement-learning parameter optimization method is proposed for variable-speed trajectory tracking control of underactuated vessels under scenarios involving model uncertainties and external environmental disturbances. …”
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A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination by Xiaoke Zhou, Fei Zhu, Quan Liu, Yuchen Fu, Wei Huang

“…Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. …”
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EdgeGuard: Decentralized Medical Resource Orchestration via Blockchain-Secured Federated Learning in IoMT Networks by Sakshi Patni, Joohyung Lee

“…We have made several technological advances, including a lightweight blockchain consensus mechanism designed for IoMT networks, an adaptive edge resource allocation method based on reinforcement learning, and a federated learning algorithm optimized for medical data with differential privacy. …”
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Model-Based Detection of Coordinated Attacks (DCA) in Distribution Systems by Nitasha Sahani, Chen-Ching Liu

“…In this paper, a novel proactive DCA strategy is proposed for early detection of CCA by establishing correlations among distinct attack events through model-based reinforcement learning that utilizes abductive reasoning to conclude the attacker goal. …”
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