Huaxin Pei
Papers
1
Total Citations
3
H-Index
1
About
Huaxin Pei is a researcher advancing the frontiers of multi-agent reinforcement learning (MARL), with a particular focus on building fault-tolerant systems for complex, real-world applications. Their work addresses a critical gap: how to maintain performance when individual agents in a multi-agent system experience unexpected failures. Pei’s key contributions include identifying and tackling two core challenges posed by agent faults—the difficulty of extracting meaningful information from chaotic state spaces and the problem of learning from transitions recorded before faults occur. Their 2025 paper, “Toward Fault Tolerance in Multi-Agent Reinforcement Learning,” which has already garnered 3 citations, lays the groundwork for more resilient MARL algorithms. This research is vital for deploying MARL in safety-critical domains like autonomous drone swarms, robotic teams, and distributed sensor networks, where agent failures are inevitable. By pioneering methods to handle these disruptions, Huaxin Pei is helping to ensure that multi-agent systems can operate reliably in unpredictable environments, making their work highly relevant for students and researchers interested in robust AI, distributed control, and the practical deployment of reinforcement learning.
Research Focus
Key Achievements
Top Papers
- 1Toward Fault Tolerance in Multi-Agent Reinforcement Learning3 citations · 2025