Shuxin Huo
Papers
2
Total Citations
54
H-Index
2
About
Shuxin Huo is a rising researcher in artificial intelligence and multi-robot systems, with a primary focus on deep reinforcement learning and cooperative decision-making. His most impactful work, "Hybrid attention-oriented experience replay for deep reinforcement learning and its application to a multi-robot cooperative hunting problem" (2022), has garnered 43 citations, introducing a novel attention mechanism to improve sample efficiency in multi-agent environments. Building on this, Huo developed "Cooperative offensive decision-making for soccer robots based on bi-channel Q-value evaluation MADDPG" (2023), which advances multi-agent reinforcement learning by integrating dual-channel value evaluation for more strategic team coordination. His contributions are particularly notable for bridging theoretical reinforcement learning advances with practical robotic applications, such as cooperative hunting and soccer robot tactics. By addressing key challenges in experience replay and multi-agent credit assignment, Huo's work provides foundational methods for scalable, real-time decision-making in dynamic, cooperative settings—making his research highly relevant for students and engineers working on autonomous systems, swarm robotics, and intelligent game AI.
Research Focus
Key Achievements
Top Papers
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- 2