Shinichi Honiden
Papers
3
Total Citations
17
H-Index
3
About
Shinichi Honiden is a leading researcher in self-adaptive systems and reinforcement learning, focusing on creating intelligent, autonomous systems that can dynamically adjust to changing environments. His major contributions lie in integrating meta-reinforcement learning and neuroevolution to enable systems to learn and optimize adaptation policies without extensive prior assumptions. Honiden’s work on self-learning adaptive systems (SLAS), particularly his 2021 paper with 10 citations, demonstrates how machine learning can enhance system adaptability in dynamic settings. He has also advanced sample efficiency in robotics, as shown in his 2013 study on neuroevolution algorithms for quadruped robots (4 citations), and introduced innovative knowledge reuse strategies through curriculum evolution for reinforcement learning-based adaptation (2022, 3 citations). Honiden’s research bridges the gap between theoretical reinforcement learning and practical self-adaptive systems, offering scalable solutions for real-world applications like robotics and autonomous software. His work is highly relevant for students and researchers exploring adaptive AI, policy optimization, and lifelong learning in complex, evolving environments.
Research Focus
Key Achievements
Top Papers
- 1A Meta Reinforcement Learning-based Approach for Self-Adaptive System10 citations · 2021
- 2
- 3