Syunsuke MAEDA

About

Syunsuke Maeda’s research focuses on reinforcement learning and neural network architectures for robotic control, with a particular emphasis on state-space segmentation and adaptive learning systems. His most cited work, “Reinforcement Learning of Multi-Link Robot with Fuzzy ART Neural Networks for State-Space Segmentation” (2005, 3 citations), introduces an innovative approach that applies Fuzzy ART neural networks as state discriminators in reinforcement learning. Maeda demonstrates that by incrementally expanding the state space, learning speed improves while conserving hardware resources. Furthermore, he shows that when creating new category units for novel states, transferring state values from similar existing units significantly enhances learning performance—a finding validated through both simulation and physical experiments. This work represents a meaningful contribution to efficient, resource-aware robot learning, offering a practical framework for adaptive control in multi-link robotic systems. While his citation count is modest, Maeda’s research provides foundational insights into neuro-adaptive reinforcement learning, particularly relevant for students and researchers exploring incremental learning and hardware-constrained robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   Reinforcement Learning of Multi-Link Robot with Fuzzy ART Neural Networks for State-Space Segmentation

   3 citations · 2005

Key Collaborators

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