Global and Local Awareness: Combine Reinforcement Learning and Model-Based Control for Collision Avoidance by Luman Zhao, Guoyuan Li, Houxiang Zhang

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Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research by Jan Benad, Frank Röder, Manfred Eppe

Subjects: “…Reinforcement learning…”
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Orthogonal Capsule Network with Meta-Reinforcement Learning for Small Sample Hyperspectral Image Classification by Prince Yaw Owusu Amoako, Guo Cao, Boshan Shi, Di Yang, Benedict Boakye Acka

“…To address this issue, we propose an innovative model that combines an orthogonal capsule network with meta-reinforcement learning (OCN-MRL) for small sample HSIC. The OCN-MRL framework employs Meta-RL for feature selection and CapsNet for classification with a small data sample. …”
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Intelligent IoT-Based Network Clustering and Camera Distribution Algorithm Using Reinforcement Learning by Islam T. Almalkawi, Rami Halloush, Mohammad F. Al-Hammouri, Alaa Alghazo, Loiy Al-Abed, Mohammad Amra, Ayooub Alsarhan, Sami Aziz Alshammari

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ARCS: Adaptive Reinforcement Learning Framework for Automated Cybersecurity Incident Response Strategy Optimization by Shaochen Ren, Jianian Jin, Guanchong Niu, Yang Liu

Subjects: “…reinforcement learning…”
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Joint Adaptive OFDM and Reinforcement Learning Design for Autonomous Vehicles: Leveraging Age of Updates by Mamady Delamou, Ahmed Naeem, Huseyin Arslan, El Mehdi Amhoud

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Reinforcement learning based route optimization model to enhance energy efficiency in internet of vehicles by Quadeer Hussain, Ahmad Shukri Mohd Noor, Muhammad Mukhtar Qureshi, Jianqiang Li, Atta-ur Rahman, Aghiad Bakry, Tariq Mahmood, Amjad Rehman

“…In the realm of IoV, we propose OptiE2ERL, an advanced Reinforcement Learning (RL) based model designed to optimize energy efficiency and routing. …”
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A cooperative jamming decision-making method based on multi-agent reinforcement learning by Bingchen Cai, Haoran Li, Naimin Zhang, Mingyu Cao, Han Yu

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A deep reinforcement learning-based approach for cyber resilient demand response optimization by Ayush Sinha, Ranjana Vyas, Feras Alasali, William Holderbaum, O. P. Vyas

“…This research endeavors to advance peak load forecasting strategies and demand response optimization at the microgrid level, thereby enhancing grid reliability through the application of Deep Reinforcement Learning (DRL) techniques. Additionally, it investigates the ongoing threat of false data injection attacks. …”
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Electrical vehicle grid integration for demand response in distribution networks using reinforcement learning by Fayiz Alfaverh, Mouloud Denaï, Yichuang Sun

“…Here, an effective DR approach for V2G and V2H energy management using Reinforcement Learning (RL) is proposed. Q‐learning, an RL strategy based on a reward mechanism, is used to make optimal decisions to charge or delay the charging of the EV battery pack and/or dispatch the stored electricity back to the grid without compromising the driving needs. …”
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Deep Reinforcement Learning-Based Anti-Jamming Approach for Fast Frequency Hopping Systems by Sixi Cheng, Xiang Ling, Lidong Zhu

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Two-stage deep reinforcement learning method for agile optical satellite scheduling problem by Zheng Liu, Wei Xiong, Zhuoya Jia, Chi Han

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Memory-driven deep-reinforcement learning for autonomous robot navigation in partially observable environments by Estrella Montero, Nabih Pico, Mitra Ghergherehchi, Ho Seung Song

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A Model for Evolution of Investors Behavior in Stock Market Based on Reinforcement Learning in Network by Xiaqun Liu, Yaming Zhuang, Jinsheng Li

“…This paper builds an evolution model of investors behavior based on the reinforcement learning in multiplex networks. Due to the heterogeneity of learning characteristics of bounded rational investors in investment decisions, we consider, respectively, the evolution mechanism of individual investors and institutional investors on the complex network theory and reinforcement learning theory. …”
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Moor: Model-based offline policy optimization with a risk dynamics model by Xiaolong Su, Peng Li, Shaofei Chen

Subjects: “…Offline Reinforcement Learning…”
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Enhancing Channel Selection in 5G with Decentralized Federated Multi-Agent Deep Reinforcement Learning by Taghi Shahgholi, Keyhan Khamforoosh, Amir Sheikhahmadi, Sadoon Azizi

“…In this paper, we present a comprehensive study on channelization in Cellular Vehicle-to-Everything (C-V2X) communication and propose a novel two-layer multi-agent approach that integrates deep reinforcement learning (DRL) and federated learning (FL) to enhance the decision-making process in channel utilization.Our approach leverages the autonomy of each vehicle, treating it as an independent agent capable of making channel selection decisions based on its local observations in its own cluster. …”
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Control of Magnetic Manipulator Using Reinforcement Learning Based on Incrementally Adapted Local Linear Models by Martin Brablc, Jan Žegklitz, Robert Grepl, Robert Babuška

“…Reinforcement learning (RL) agents can learn to control a nonlinear system without using a model of the system. …”
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A Reinforcement Learning Based Traffic Control Strategy in a Macroscopic Fundamental Diagram Region by Lingyu Zheng, Bing Wu

“…The development of reinforcement learning (RL) makes it possible to apply feedback to UTCS, and great efforts have been made on RL-based traffic control strategies. …”
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Safety-Critical Trajectory Tracking Control with Safety-Enhanced Reinforcement Learning for Autonomous Underwater Vehicle by Tianli Li, Jiaming Tao, Yu Hu, Shiyu Chen, Yue Wei, Bo Zhang

“…This paper investigates a novel reinforcement learning (RL)-based quadratic programming (QP) method for the safety-critical trajectory tracking control of autonomous underwater vehicles (AUVs). …”
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SGD-TripleQNet: An Integrated Deep Reinforcement Learning Model for Vehicle Lane-Change Decision by Yang Liu, Tianxing Yang, Liwei Tian, Jianbiao Pei

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