Special Issue on Cutting Edge of Reinforcement Learning and its Hybrid Methods

Abstract

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.