Neuroevolutionary reinforcing learning of neural networks by Y. A. Bury, D. I. Samal

Subjects: Get full text

Reinforcement learning for deep portfolio optimization by Ruyu Yan, Jiafei Jin, Kun Han

Subjects: Get full text

Reinforcement Learning for Reconfigurable Robotic Soccer by M. Shameer Ahamed, J. J. J. Pey, S. M. Bhagya P. Samarakoon, M. A. Viraj J. Muthugala, Mohan Rajesh Elara

Subjects: Get full text

Research on the Application of Reinforcement Learning in Traffic Flow Prediction by Hu Yiquan

“…The purpose of this paper is to discuss how Reinforcement Learning (RL) can be applied to TFP. RL optimizes strategies through interactions between agents and the environment to maximize cumulative rewards. …”
Get full text

Intelligent Inventory Control via Ruminative Reinforcement Learning by Tatpong Katanyukul, Edwin K. P. Chong

“…Ruminative reinforcement learning (RRL) has been introduced recently based on this approach. …”
Get full text

Physics-guided actor-critic reinforcement learning for swimming in turbulence by Christopher Koh, Laurent Pagnier, Michael Chertkov

“…We explore optimally balancing these efforts by developing a novel physics-informed reinforcement learning strategy and comparing it with prescribed control and physics-agnostic reinforcement learning strategies. …”
Get full text

Music Emotion Research Based on Reinforcement Learning and Multimodal Information by Yue Hu

“…In this paper, a multimodal fusion algorithm for music emotion analysis is proposed, and a dynamic model based on reinforcement learning is constructed to improve the analysis accuracy. …”
Get full text

Drone Landing and Reinforcement Learning: State-of-Art, Challenges and Opportunities by Jose Amendola, Linga Reddy Cenkeramaddi, Ajit Jha

Subjects: “…Deep reinforcement learning…”
Get full text

Application of Reinforcement Learning in Cognitive Radio Networks: Models and Algorithms by Kok-Lim Alvin Yau, Geong-Sen Poh, Su Fong Chien, Hasan A. A. Al-Rawi

“…Cognitive radio (CR) enables unlicensed users to exploit the underutilized spectrum in licensed spectrum whilst minimizing interference to licensed users. Reinforcement learning (RL), which is an artificial intelligence approach, has been applied to enable each unlicensed user to observe and carry out optimal actions for performance enhancement in a wide range of schemes in CR, such as dynamic channel selection and channel sensing. …”
Get full text

Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning by Sung-Jung Wang, S. K. Jason Chang

“…The experimental results indicate that the developed algorithm outperforms other reinforcement learning methods in the multi-agent domain. …”
Get full text

RL4CEP: reinforcement learning for updating CEP rules by Afef Mdhaffar, Ghassen Baklouti, Yassine Rebai, Mohamed Jmaiel, Bernd Freisleben

Subjects: “…Deep Reinforcement Learning…”
Get full text

Nonlinear Waveform Sensing for Cognitive Radar Based on Reinforcement Learning by Peikun Zhu, Xu Si, Jiachen Han, Jing Liang

“…In this work, we propose an NLFM cognitive radar based on reinforcement learning for target sensing. Specifically, a radar waveform selection framework is proposed via the interactive multimodel. …”
Get full text

Harnessing Meta-Reinforcement Learning for Enhanced Tracking in Geofencing Systems by Alireza Famili, Shihua Sun, Tolga Atalay, Angelos Stavrou

“…In this paper, we introduce MetaFence: Meta-Reinforcement Learning for Geofencing Enhancement, a novel approach for precise geofencing utilizing indoor 5G small cells, termed “5G Points”, which are optimally deployed using a meta-reinforcement learning (meta-RL) framework. …”
Get full text

Review and outlook on reinforcement learning: Its application in agricultural energy internet by Xueqian Fu, Jing Zhang, Xiang Bai, Xinyue Chang, Yixun Xue

“…Furthermore, the authors examine the critical technologies of reinforcement learning in the context of smart grid applications. …”
Get full text

Decentralized Reinforcement Learning Approach for Microgrid Energy Management in Stochastic Environment by Razieh Darshi, Saeed Shamaghdari, Aliakbar Jalali, Hamidreza Arasteh

“…This paper proposes a fully decentralized multiagent Energy Management System (EMS) for microgrids using the reinforcement learning and stochastic game. The microgrid agents, comprising customers, and DERs are considered as intelligent and autonomous decision makers. …”
Get full text

The Application of Reinforcement Learning in Traffic Flow Prediction: Advantages, Problems, and Prospects by Li Minghui, Zhou Decheng, Zhang Shiqi

“…Concurrently, as science and technology advance, a growing variety of academics are attempting to incorporate reinforcement learning (RL) into TFP. Experimental results show that it can reduce vehicle queuing time and average delay to a greater extent, and alleviate air pollution. …”
Get full text

Review: the application of deep reinforcement learning to quantitative trading in financial market by XU Bo, HE Yijun, WEN Jiancheng, LI Xiangxia

Subjects: Get full text

Reinforcement Learning-Based Generative Security Framework for Host Intrusion Detection by Yongsik Kim, Su-Youn Hong, Sungjin Park, Huy Kang Kim

Subjects: “…Reinforcement learning…”
Get full text

Reinforcement Learning with Probabilistic Boolean Network Models of Smart Grid Devices by Pedro Juan Rivera Torres, Carlos Gershenson García, María Fernanda Sánchez Puig, Samir Kanaan Izquierdo

“…This work demonstrates that PBNs are equivalent to the standard Reinforcement Learning Cycle, in which the agent/model has an interaction with its environment and receives feedback from it in the form of a reward signal. …”
Get full text

Deep gradient reinforcement learning for music improvisation in cloud computing framework by Fadwa Alrowais, Munya A. Arasi, Saud S. Alotaibi, Mohammed Alonazi, Radwa Marzouk, Ahmed S. Salama

Subjects: “…Reinforcement learning…”
Get full text