Web: http://arxiv.org/abs/2209.10078

Sept. 22, 2022, 1:11 a.m. | Shuting Kang, Heng Guo, Yunzhi Xue

cs.LG updates on arXiv.org arxiv.org

Critical scenario generation requires the ability of finding critical
parameter combinations from the infinite parameter space in the logic scenario.
Existing solutions aims to explore the correlation of parameters in the initial
scenario without considering the connection between the parameters in the
action sequence. How to model action sequences and consider the effects of
different action parameter in the scenario remains a key challenge to solve the
problem. In this paper, we propose a framework to generate critical scenarios
for …

arxiv driving framework language reinforcement reinforcement learning

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