In reinforcement learning, policy gradient algorithms optimize the policy directly and rely on sampling efficiently an environment. Nevertheless, while most sampling procedures are based on direct policy sampling, self-performance measures could be used to improve such sampling prior to each policy update. Following this line of thought, we introduce SAUNA, a method where non-informative transitions are rejected from the gradient update. The level of information is estimated according to the fraction of variance explained by the value function: a measure of the discrepancy between V and the empirical returns. In this work, we use this metric to select samples that are useful to learn from, and we demonstrate that this selection can significantly improve the performance of policy gradient methods. In this paper: (a) We define SAUNA's metric and introduce its method to filter transitions. (b) We conduct experiments on a set of benchmark continuous control problems. SAUNA significantly improves performance. (c) We investigate how SAUNA reliably selects samples with the most positive impact on learning and study its improvement on both performance and sample efficiency.
State Estimation Using Particle Filtering in Adaptive Machine Learning Methods: Integrating Q-Learning and NEAT Algorithms with Noisy Radar Measurements
A Bayesian Filtering Approach for Learning Lagrangian Dynamics from Noisy Measurements
School of Engineering and Applied Science, Ahmedabad University（艾哈迈达巴德大学工程与应用科学学院）；Department of Electrical Engineering, Indian Institute of Technology (BHU) Varanasi（印度理工学院（BHU）瓦拉纳西分校电气工程系）；ELLIS Institute Finland（芬兰ELLIS研究所）；Department of Electrical Engineering and Automation, Aalto University（阿尔托大学电气工程与自动化系）
Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data
Computer Engineering Department, İstanbul Commerce University（伊斯坦布尔商业大学计算机工程系）；Turkcell Teknoloji（Turkcell技术公司）；Grid Communications and Security Group, Electrification and Energy Infrastructures Division, Oak Ridge National Laboratory（橡树岭国家实验室电网安全与能源基础设施部门）
针对联邦学习中的噪声标签、类别缺失与不均衡问题，提出结合自适应噪声清洗、条件 GAN 合成补全和鲁棒训练的隐私保护框架，并在 MNIST 与 Fashion-MNIST 上验证其性能提升。
Self-Boosting Vision-Language Models with Noisy Student On-Policy Self-Distillation
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；ByteDance Inc.（字节跳动公司）；Zhejiang University of Technology（浙江工业大学）；Hong Kong Baptist University（香港浸会大学）
ITBoost: Information-Theoretic Trust for Robust Boosting
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Department of Software Engineering, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada（软件工程系，安达卢西亚数据科学与计算智能研究所（DaSCI），格拉纳达大学）；College of Information Science and Technology, Beijing University of Chemical Technology（信息科学与技术学院，北京化工大学）
