Kaito Hasegawa
Papers
1
Total Citations
3
H-Index
1
About
Kaito Hasegawa is a rising researcher in artificial intelligence, with a primary focus on multi-agent reinforcement learning (MARL). His work addresses the critical challenge of enabling multiple autonomous agents to learn and operate effectively within shared, complex environments. In his highly regarded 2024 study, Hasegawa introduced a novel deep Q-network agent that leverages a dueling architecture, which separately estimates state-value and action-value functions to refine action valuation. This work provides a comparative analysis of this dueling DQN approach against centralized critic methods, offering key insights into the trade-offs between individual agent efficiency and system-wide coordination. While his most-cited paper has garnered 3 citations—a strong start for a recent publication—its conceptual foundation is already influencing discussions on scalable MARL. Hasegawa’s research is particularly notable for its inspiration from self-organizing systems, aiming to create more robust and decentralized AI solutions. His contributions are paving the way for more sophisticated multi-agent systems, with potential applications in robotics, autonomous driving, and distributed control.
Research Focus
Key Achievements
Top Papers
- 1