Conference Information

ISRL 2020: International Symposium on Reinforcement Learning

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Submission Date:
2020-09-01 Extended
Notification Date:
2020-09-15
Conference Date:
2020-11-06
Location:
Washington DC, USA
Years:
4
Viewed: 13429   Tracked: 0   Attend: 0

Call For Papers

ISRL 2020 (International Symposium on Reinforcement Learning) is an academic conference held in Washington DC, USA on 2020-11-06. The paper submission deadline is 2020-09-01 (extended). Acceptance notifications are sent on 2020-09-15.

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. Call for Papers IEEE ISRL 2020 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 2020 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
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