Manipulating Reinforcement Learning: Stealthy Attacks on Cost Signals
暂无分享,去创建一个
[1] Quanyan Zhu,et al. Adaptive Honeypot Engagement through Reinforcement Learning of Semi-Markov Decision Processes , 2019, GameSec.
[2] Quanyan Zhu,et al. Deceptive Reinforcement Learning Under Adversarial Manipulations on Cost Signals , 2019, GameSec.
[3] Arslan Munir,et al. Adversarial Reinforcement Learning Framework for Benchmarking Collision Avoidance Mechanisms in Autonomous Vehicles , 2018, IEEE Intelligent Transportation Systems Magazine.
[4] Marc G. Bellemare,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[5] Jan Peters,et al. Reinforcement learning in robotics: A survey , 2013, Int. J. Robotics Res..
[6] T. Urbanik,et al. Reinforcement learning-based multi-agent system for network traffic signal control , 2010 .
[7] Quanyan Zhu,et al. Dynamic policy-based IDS configuration , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.
[8] Ah-Hwee Tan,et al. Integrating Temporal Difference Methods and Self-Organizing Neural Networks for Reinforcement Learning With Delayed Evaluative Feedback , 2008, IEEE Transactions on Neural Networks.
[9] D. Ernst,et al. Power systems stability control: reinforcement learning framework , 2004, IEEE Transactions on Power Systems.
[10] C. G. Broyden. On theorems of the alternative , 2001 .
[11] Peter Dayan,et al. Q-learning , 1992, Machine Learning.
[12] Sean P. Meyn,et al. The O.D.E. Method for Convergence of Stochastic Approximation and Reinforcement Learning , 2000, SIAM J. Control. Optim..
[13] Benjamin Van Roy,et al. An Analysis of Temporal-Difference Learning with Function Approximation , 1998 .