Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning by Hsuan-Ta Lin, Po-Ming Lee, Tzu-Chien Hsiao

“…Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. …”
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Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning by Duowei Li, Jianping Wu, Ming Xu, Ziheng Wang, Kezhen Hu

“…., “Simulation of Urban Mobility” (SUMO), using the technology of modern deep reinforcement learning. The model is proposed based on a deep Q-network algorithm that precisely represents the elements associated with the problem: agents, environments, and actions. …”
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A Deep Reinforcement Learning Approach to the Optimization of Data Center Task Scheduling by Haiying Che, Zixing Bai, Rong Zuo, Honglei Li

“…This study aimed at building a new scheduling model with deep reinforcement learning algorithm, which integrated the task scheduling with resource-utilization optimization. …”
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OPTIMIZING AUTONOMOUS VEHICLE PATH PLANNING USING REINFORCEMENT LEARNING AND DYNAMIC MAPPING by Sundaram Arumugam, Frank Stomp

“…This research examines the combination of Reinforcement Learning (RL) with dynamic mapping to enhance route planning in autonomous vehicles (AVs). …”
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A Reward Optimization Method Based on Action Subrewards in Hierarchical Reinforcement Learning by Yuchen Fu, Quan Liu, Xionghong Ling, Zhiming Cui

“…Reinforcement learning (RL) is one kind of interactive learning methods. …”
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Superconducting quantum computing optimization based on multi-objective deep reinforcement learning by Yangting Liu

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Deep reinforcement learning for active flow control in a turbulent separation bubble by Bernat Font, Francisco Alcántara-Ávila, Jean Rabault, Ricardo Vinuesa, Oriol Lehmkuhl

“…Abstract The control efficacy of deep reinforcement learning (DRL) compared with classical periodic forcing is numerically assessed for a turbulent separation bubble (TSB). …”
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Multi-Objective Dynamic Path Planning with Multi-Agent Deep Reinforcement Learning by Mengxue Tao, Qiang Li, Junxi Yu

Subjects: “…multi-agent reinforcement learning…”
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Deep Reinforcement Learning-Based Speed Predictor for Distributionally Robust Eco-Driving by Rajan Chaudhary, Nalin Kumar Sharma, Rahul Kala, Sri Niwas Singh

Subjects: “…Deep reinforcement learning…”
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Global Information Management Model and Identification Method Based on Deep Reinforcement Learning by RuiLe Lan, XiJia He

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Retracted: UAV Mission Path Planning Based on Reinforcement Learning in Dynamic Environment by Journal of Function Spaces

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Enhancing cooperation in dynamic networks through reinforcement-learning-based rewiring strategies by Hsuan-Wei Lee, Szu-Ping Chen, Feng Shi

Subjects: “…reinforcement learning…”
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ReMAV: Reward Modeling of Autonomous Vehicles for Finding Likely Failure Events by Aizaz Sharif, Dusica Marijan

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A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data by Bing Liu, Yu Tang, Yuxiong Ji, Yu Shen, Yuchuan Du

“…In this work, we propose a deep reinforcement learning (DRL) method to explore the potential of traffic video data in improving the efficiency of ramp metering. …”
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Dueling Network Architecture for GNN in the Deep Reinforcement Learning for the Automated ICT System Design by Tianchen Zhou, Yutaka Yakuwa, Natsuki Okamura, Hiroyuki Hochigai, Takayuki Kuroda, Ikuko Eguchi Yairi

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IALight: Importance-Aware Multi-Agent Reinforcement Learning for Arterial Traffic Cooperative Control by Lu WEI, Xiaoyan ZHANG, Lijun FAN, Lei GAO, Jian YANG

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Preference learning based deep reinforcement learning for flexible job shop scheduling problem by Xinning Liu, Li Han, Ling Kang, Jiannan Liu, Huadong Miao

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Multiagent Reinforcement Learning-Based Taxi Predispatching Model to Balance Taxi Supply and Demand by Yongjian Yang, Xintao Wang, Yuanbo Xu, Qiuyang Huang

“…To solve this problem, we propose a Multiagent Reinforcement Learning- (MARL-) based taxi predispatching model through analyzing the running data of 13,000 taxis. …”
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Developing Train Station Parking Algorithms: New Frameworks Based on Fuzzy Reinforcement Learning by Wei Li, Kai Xian, Jiateng Yin, Dewang Chen

“…To increase the parking accuracy, robustness, and self-learning ability, we propose new train station parking frameworks by using the reinforcement learning (RL) theory combined with the information of balises. …”
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Multi-tenant computing network resource allocation algorithm based on deep reinforcement learning by HU Yuxiang, FENG Xu, DONG Yongji, HE Mengyang, ZHUANG Lei, SONG Yanrui

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