Learning Normalizing Flow Policies Based on Highway Demonstrations

2021 IEEE International Intelligent Transportation Systems Conference (ITSC)(2021)

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Abstract
Imitation learning on real-world data has the potential to improve the simulation of real-world traffic. However, learning from human demonstrations can be challenging since the recorded behavior is typically noisy and multimodal. Most policies used in such setups parameterize a Gaussian distribution which is then used for sampling actions. This limits the expressiveness of the outputs and more so...
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Key words
Road transportation,Conferences,Benchmark testing,Gaussian distribution,Data models,Entropy,Noise measurement
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