Explaining a Deep Reinforcement Learning (DRL)-Based Automated Driving Agent in Highway Simulations

Explaining a Deep Reinforcement Learning (DRL)-Based Automated Driving Agent in Highway Simulations

As deep learning models have become increasingly complex, it is critical to understand their decision-making, particularly in safety-relevant applications. In order to support a quantitative interpretation of an autonomous agent trained through Deep Reinforcement Learning (DRL) in the highway-env si...

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Bibliographic Details
Main Authors: Francesco Bellotti, Luca Lazzaroni, Alessio Capello, Marianna Cossu, Alessandro De Gloria, Riccardo Berta
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Automated driving
deep Q learning
explainable AI
highway simulation environment
reinforcement learning
Online Access:https://ieeexplore.ieee.org/document/10077125/

Internet

https://ieeexplore.ieee.org/document/10077125/

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