We are pleased to announce that the JACIII Awards of 2024 have been decided by the JACIII editorial board. This year, the award winning papers were severely and fairly selected among 329 papers published in JACIII Vols.25 (2021) to 27 (2023) and there was no entries that deserved the Best Review Paper award. The award ceremony was held on September 30, 2024 in hybrid style (both on-site and online; venue: Gakushikaikan, Tokyo, Japan), attended by the authors and JACIII editorial board members who took part in the selection process. We congratulate the winners and sincerely wish them success in the future.

JACIII BEST PAPER AWARD 2024

Yuri Murayama and Ichiro Kobayashi

Toward Question-Answering with Multi-Hop Reasoning and Calculation over Knowledge Using a Neural Network Model with External Memories

JACIII Vol.27 No.3, pp. 481-489, 2023.

JACIII YOUNG RESEARCHER AWARD 2024

Takenori Obo

Human Pose Estimation with Multi-Camera Localization Using Multi-Objective Optimization Based on Topological Structured Learning

JACIII Vol.27 No.4, pp. 543-553, 2023.

The JACIII Distinguished Editor and Outstanding Reviewer Awards were established for the purpose to honor editors who have made a significant contribution to the growth of the JACIII in 2018 and to acknowledge reviewers who have made a significant contribution to reviewing in 2023. We express our deepest gratitude for their professional work, which we believe conductive to development of not only the JACIII but also scientific research.

Since deep Q-networks and AlphaGO by Google DeepMind were proposed, not only a reinforcement learning integrated with deep learning but also its applications have attracted much attention on. ChatGPT, which learns by Proximal Policy Optimization as one of reinforcement learning mechanisms, is an excellent example.

With this background, we propose the special issue titled “Cutting Edge of Reinforcement Learning and its Hybrid Methods,” which is the same of the special issue in 2017, to cover the latest progress and trends in reinforcement learning and its hybrid methods (combined with machine learning, neural networks, and evolutionary computation).

We received 28 submissions, out of which 12 submissions were peer-reviewed and 16 submissions that were outside the scope of the special issue were excluded. After a thorough review process including comments and suggestions by reviewers, seven submissions were accepted. These included four theory and three application papers, indicating nearly equal contributions in theory and application aspects.

The content of the first three papers is related to inverse reinforcement learning, while the fourth one is not directly related to reinforcement learning but it would be related to reinforcement learning in the future. All papers address issues surrounding cutting-edge technology in reinforcement learning.

We would like to end by expressing that we hope and believe that this special issue can largely contribute to the development in the field of reinforcement learning while holding a wide international appeal.