Deep Reinforcement Learning (DRL) has achieved remarkable advances in sequential decision tasks. However, recent works have revealed that DRL agents are susceptible to slight perturbations in observations. This vulnerability raises concerns regarding the effectiveness and robustness of deploying such agents in real-world applications. In this work, we propose a novel robust reinforcement learning method called SortRL, which improves the robustness of DRL policies against observation perturbations from the perspective of the network architecture. We employ a novel architecture for the policy network that incorporates global $l_\infty$ Lipschitz continuity and provide a convenient method to enhance policy robustness based on the output margin. Besides, a training framework is designed for SortRL, which solves given tasks while maintaining robustness against $l_\infty$ bounded perturbations on the observations. Several experiments are conducted to evaluate the effectiveness of our method, including classic control tasks and video games. The results demonstrate that SortRL achieves state-of-the-art robustness performance against different perturbation strength.
Global Convergence of Direct Policy Search for State-Feedback $\mathcal{H}_\infty$ Robust Control: A Revisit of Nonsmooth Synthesis with Goldstein Subdifferential
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Robustness and Generalization in Quantum Reinforcement Learning via Lipschitz Regularization
Fraunhofer IIS, Fraunhofer Institute for Integrated Circuit IIS（弗劳恩霍夫集成电路研究所（弗劳恩霍夫IIS））；Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔兰根-纽伦堡大学模式识别实验室）；University of Stuttgart（斯图加特大学）
Policy Verification in Stochastic Dynamical Systems Using Logarithmic Neural Certificates
University of Oxford, Oxford, United Kingdom（牛津大学）；Radboud University, Nijmegen, the Netherlands（拉德博德大学）；Lund University, Lund, Sweden（隆德大学）；University of Copenhagen, Copenhagen, Denmark（哥本哈根大学）；Ruhr-University Bochum, Bochum, Germany（波鸿鲁尔大学）
