Seokho Yang

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About

Seokho Yang is a rising researcher in the field of robotics and artificial intelligence, with a primary focus on deep reinforcement learning (DRL) for multi-agent systems. His most notable contribution is the development of a novel Deep Q-Network (DQN) algorithm for coordinating multiple mobile robots, enabling them to autonomously sense their environment and navigate optimized paths. In this seminal 2023 work, Yang innovatively used the collective states of multiple robots as direct inputs to the DQN, allowing the system to estimate Q-values and select optimal actions for the entire fleet. This approach addresses critical challenges in decentralized robotic control, offering a scalable solution for complex tasks like warehouse logistics and search-and-rescue operations. While his work has garnered 10 citations to date, reflecting its early-stage impact, the practical significance of his DRL framework positions it as a foundational contribution for future multi-robot navigation systems. Yang’s research bridges the gap between theoretical reinforcement learning and real-world robotic applications, marking him as a promising voice in the advancement of intelligent, autonomous multi-agent coordination.