Ren Katayama
Papers
1
Total Citations
5
H-Index
1
About
Ren Katayama is a researcher whose work lies at the intersection of reinforcement learning, transfer learning, and cognitive-inspired AI systems. Their most notable contribution is the development of a novel automatic policy selection method that leverages spreading activation theory—a concept rooted in psychological models of human memory and cognition—to improve transfer learning in reinforcement learning. This approach, detailed in their 2019 paper "Activation and Spreading Sequence for Spreading Activation Policy Selection Method in Transfer Reinforcement Learning," addresses a critical challenge in intelligent robotics: enabling systems like home robots, communication robots, and warehouse automation to adapt more efficiently across different tasks by reusing learned policies. While their work has garnered modest citation counts to date, its interdisciplinary nature—bridging psychology and machine learning—positions it as a foundational piece for researchers exploring biologically plausible mechanisms in AI. Katayama’s research is particularly relevant for advancing autonomous systems that require flexible, human-like learning and adaptation in real-world environments, making their contributions a valuable resource for students and researchers working on transfer learning, robotic control, and cognitive architectures.
Research Focus
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Top Papers
- 1