JACIII Vol.28 No.2 (Mar. 2024) Special Issue on
“Cutting Edge of Reinforcement Learning and its Hybrid Methods”
Submission Deadline: June 18, 2023（和文投稿締切：2023年6月4日）
|Editors:||Prof. Kazuteru Miyazaki (National Institution for Academic Degrees and Quality Enhancement of Higher Education, Japan)|
Prof. Keiki Takadama (Univ. of Electro-Communications, Japan)
|Inquiry:||JACIII Contact form or e-mail to (JACIII Editorial Office)|
Recently, a reinforcement learning improves its potential by integrating with deep learning, e.g., deep Q-networks (DQN) and AlphaGO proposed by Google DeepMind. To explore new potential of reinforcement learning, this special issue focuses on both reinforcement learning and its hybrid methods (such as machine learning, neural network, evolutionary computation). Research on meta-learning, multitask learning, curriculum learning, intrinsic motivation, reward shaping for a reinforcement learning is also welcome in addition to theoretical and application research.
*Reinforcement learning (including multiagent reinforcement learning and inverse reinforcement learning)
We welcome researchers to submit their achievement to this special issue as an opportunity for publishing in a high standard international journal.