Multiple-Model Based Defense for Deep Reinforcement Learning Against Adversarial Attack - researchr publication authors

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Patrick P. K. Chan, Yaxuan Wang, Natasha Kees, Daniel S. Yeung. Multiple-Model Based Defense for Deep Reinforcement Learning Against Adversarial Attack. In Igor Farkas, Paolo Masulli, Sebastian Otte, Stefan Wermter, editors, Artificial Neural Networks and Machine Learning - ICANN 2021 - 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14-17, 2021, Proceedings, Part I. Volume 12891 of Lecture Notes in Computer Science, pages 42-53, Springer, 2021. [doi]

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