With the development of Artificial Intelligence (AI) technology, many AI-enabled algorithms have deeply changed our daily lives. Reinforcement Learning (RL) algorithms are intensively studied in recent years especially on advocated game domains. There are several highlights of the numerous studies of RL in various game domains presented in the very recent years. However, there are also other research works shown that the RL can be applied on many other research topics. Thus, we held this symposium on drawing a larger perpsective on what we have learned from studying RL.
The conference proceedings will be published by IEEE xplore to be indexed by EI Compendex, Web of Science, and Scopus.
Call for Papers
IEEE ISRL 2022 aims to collect recent academic achievements in novel techniques, developments, empirical studies, and new developments in reinforcement learning. Innovative technical applications based on reinforcement learning algorithms are highly encouraged. The objective of IEEE ISRL 2022 is to provide a forum for scientists, engineers, and researchers to discuss and exchange their new ideas, novel results, work in progress and experience on all aspects of reinforcement learning. Topics of particular interest include, but are not limited to:
- Current state of reinforcement learning algorithms
- Technical issues of reinforcement learning applications
- Theoretical and experimental analysis of reinforcement learning
- Security and Privacy with reinforcement learning
- Learning paradigms for reinforcement learning
- Reinforcement learning for scheduling
- Reinforcement learning based behaviour correction
- High performance computing for training with reinforcement learning
- The future applications of reinforcement learning
Submissions should include abstract, 5-10 keywords, and the e-mail address of the corresponding author and be in PDF format. Each submission should be in the IEEE 8.5 x 11 inches two-column format with 10-12 point font (Template: DOC or LaTeX), including tables, figures, and references.
- General Chair
- Meikang Qiu, Texas A&M University, USA
- Program Chair
- Cheng Zhang, Waseda University, Japan
- Zongming Fei, University of Kentucky, USA
- Technical Program Committee
- Xiaofu He, Columbia University Medical Center, USA
- Petr Matousek, Brno University of Technology, Czechia
- Peng Gou, DNVGL Software, Norway
- Yongxin Zhu, Shanghai Jiaotong University, China
- Han Qiu, Telecom-ParisTech, France
- Junjie Peng, Shanghai University, China
- Vitor Jesus, Birmingham City University, UK
- Zongming Fei, University of Kentucky, USA
- Denis Trystram, Grenoble university, France
- Guangjie Han, Beihang University, China
- Celimuge Wu, University of Electro-Communications, Japan
- Zonghua Gu, Zhejiang University, China
- Younghee Park, San Jose State University, USA
- Gang Zeng, Nagoya University, Japan
- Bo Yuan, City University of New York, USA
- Sang-Yoon Chang, Advanced digital sciences center, Singapore
- C.L. Wang, The University of Hong Kong, China
- Weigang Li, University of Brasilia, Brazil
If you have any questions or queries on ISRL 2022, please contact us via an email.
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