M. Kinoshit
Papers
1
Total Citations
3
H-Index
1
About
M. Kinoshita is a researcher in multiagent systems and reinforcement learning, with a focus on cooperative autonomous robotics. Their most notable contribution is the development of an actor-critic approach for learning cooperative behaviors, as demonstrated in their 2006 paper on multiagent seesaw balancing problems. This work proposes a novel reinforcement learning scheme that enables multiple autonomous mobile robots to collaboratively balance a seesaw—a task requiring real-time coordination and adaptation. While the paper has garnered 3 citations, its significance lies in its foundational approach to decentralized learning in cooperative multiagent environments, addressing challenges such as credit assignment and policy optimization. Kinoshita’s research bridges reinforcement learning theory and practical robotics, offering insights into how agents can learn to work together without centralized control. Their work is particularly relevant for students and researchers exploring multiagent systems, cooperative control, and adaptive robotics, providing a clear example of how actor-critic methods can be applied to complex, real-world coordination problems.
Research Focus
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Top Papers
- 1