1 citations · 1 across the 3 of their papers we have counts for
4 papers
Fault-Tolerant MARL for CAVs under Observation Perturbations for Highway On-Ramp Merging
Yuchen Shi, Huaxin Pei, Yi Zhang +1
Multi-Agent Reinforcement Learning (MARL) holds significant promise for enabling cooperative driving among Connected and Automated Vehicles (CAVs). However, its practical applicati…
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…
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…
Life-long Learning and Testing for Automated Vehicles via Adaptive Scenario Sampling as A Continuous Optimization Process
Jingwei Ge, Pengbo Wang, Cheng Chang +3
Sampling critical testing scenarios is an essential step in intelligence testing for Automated Vehicles (AVs). However, due to the lack of prior knowledge on the distribution of cr…