Robust Deep Reinforcement Learning with Adversarial Attacks Extended Abstract

被引：0
|
作者：
Pattanaik, Anay [1 ]
Tang, Zhenyi [1 ]
Liu, Shuijing [1 ]
Bommannan, Gautham [1 ]
Chowdhary, Girish [1 ]
机构：
[1] Univ Illinois, Urbana, IL 61801 USA
来源：
PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18) | 2018年
关键词：
Adversarial Machine Learning; Deep Learning; Reinforcement Learning;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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].
引用
收藏
页码：2040 / 2042
页数：3
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