most citedTowards Fault Tolerance in Multi-Agent Reinforcement Learning

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cs.LG2025

DiCriTest: Testing Scenario Generation for Decision-Making Agents Considering Diversity and Criticality

Qitong Chu, Yufeng Yue, Danya Yao +1

The growing deployment of decision-making agents in dynamic environments increases the demand for safety verification. While critical testing scenario generation has emerged as an…

cs.LG2024★ 1 cited

Towards Fault Tolerance in Multi-Agent Reinforcement Learning

Yuchen Shi, Huaxin Pei, Liang Feng +2

Agent faults pose a significant threat to the performance of multi-agent reinforcement learning (MARL) algorithms, introducing two key challenges. First, agents often struggle to e…