@inproceedings{11d00fc002814b6690e76beca1ed5a76,
title = "Robust Deep Reinforcement Learning with adversarial attacks",
abstract = "This paper proposes adversarial attacks for Reinforcement Learning (RL). These attacks are then leveraged during training to improve the robustness of RL within robust control framework. We show that this adversarial training of DRL algorithms like Deep Double Q learning and Deep Deterministic Policy Gradients leads to significant increase in robustness to parameter variations for RL benchmarks such as Mountain Car and Hopper environment. Full paper is available at (https://arxiv.org/abs/1712.03632) [7].",
keywords = "Adversarial machine learning, Deep learning, Reinforcement Learning",
author = "Anay Pattanaik and Zhenyi Tang and Shuijing Liu and Gautham Bommannan and Girish Chowdhary",
note = "Publisher Copyright: {\textcopyright} 2018 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.; 17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018 ; Conference date: 10-07-2018 Through 15-07-2018",
year = "2018",
language = "English (US)",
isbn = "9781510868083",
series = "Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS",
publisher = "International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)",
pages = "2040--2042",
booktitle = "17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018",
}