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cs.LG2024
Exploring Critical Testing Scenarios for Decision-Making Policies: An LLM Approach
Weichao Xu, Huaxin Pei, Jingxuan Yang +3
Recent advances in decision-making policies have led to significant progress in fields such as autonomous driving and robotics. However, testing these policies remains crucial with…
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…