Koopman spectral theory has provided a new perspective in the field of dynamical systems in recent years. Modern dynamical systems are becoming increasingly non-linear and complex, and there is a need for a framework to model these systems in a compact and comprehensive representation for prediction and control. The central problem in applying Koopman theory to a system of interest is that the choice of finite-dimensional basis functions is typically done apriori, using expert knowledge of the systems dynamics. Our approach learns these basis functions using a supervised learning approach where a combination of autoencoders and deep neural networks learn the basis functions for any given system. We demonstrate this approach on a simple pendulum example in which we obtain a linear representation of the non-linear system and then predict the future state trajectories given some initial conditions. We also explore how changing the input representation of the dynamic systems time series data can impact the quality of learned basis functions. This alternative representation is compared to the traditional raw time series data approach to determine which method results in lower reconstruction and prediction error of the true non-linear dynamics of the system.
Best Arm Identification with Possibly Biased Offline Data
Department of Electrical and Computer Engineering（电气与计算机工程系）；National University of Singapore（新加坡国立大学）；Department of Mathematics（数学系）；Department of Industrial Systems Engineering and Management（工业系统工程与管理系）
Systems and Control Laboratory, HUN-REN Institute for Computer Science and Control（系统与控制实验室，HUN-REN计算机科学与控制研究所）；Control Systems Group, Eindhoven University of Technology（控制系统组，埃因霍温理工大学）
Offline identification of surgical deviations in laparoscopic rectopexy
UGA（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；INSERM（法国国家健康与医学研究院）；Univ Rennes（雷恩大学）；LTSI（图像与信号处理实验室）；Michallon University Hospital（米沙隆大学医院）；CHU Grenoble（格勒诺布尔大学医院）
Disentangling Policy from Offline Task Representation Learning via Adversarial Data Augmentation
National Key Laboratory for Novel Software Technology（计算机软件新技术国家重点实验室）；School of Artificial Intelligence（人工智能学院）；Nanjing University（南京大学）；Polixir Technologies（铂乐奇科技）
Online Parameter Identification of Generalized Non-cooperative Game
Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Tongji University（同济大学）；Shanghai Research Institute for Intelligent Autonomous Systems（上海自主智能无人系统科学中心）
A Tale of Two Cities: Pessimism and Opportunism in Offline Dynamic Pricing
Department of Statistics, Florida State University（佛罗里达州立大学统计系）；Department of Management Science, University of Miami（迈阿密大学管理科学系）；Department of Decision Sciences, The George Washington University（乔治华盛顿大学决策科学系）
Adaptive Control of SE(3) Hamiltonian Dynamics with Learned Disturbance Features
University of California San Diego（加州大学圣地亚哥分校）
自适应控制是机器人在快速变化的操作条件下可靠自主的关键组成部分。自适应控制设计受益于干扰模型，这在实践中往往是不可用的。这促使使用机器学习技术来离线地从训练数据学习干扰特征，其随后可以用于在线地补偿干扰。本文开发了几何自适应控制与学习的干扰模型的刚体系统，如地面，空中和水下航行器，满足汉密尔顿的运动方程的$SE(3)$流形。我们的设计包括一个\emph{offline disturbance model identification stage}，使用汉密尔顿为基础的神经常微分方程（ODE）网络训练的状态控制轨迹数据，和一个\emph{online adaptive control stage}，估计和补偿的基础上的几何跟踪误差的干扰。我们证明了我们的自适应几何控制器的轨迹跟踪仿真全驱动摆和欠驱动四旋翼系统。
A Unified Algorithmic Framework for Hybrid Reinforcement Learning in Tabular MDPs with Shifted Transition Dynamics
Southern University of Science and Technology（南方科技大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；City University of Hong Kong（香港城市大学）；Fudan University（复旦大学）；Shanghai Academy of AI for Science（上海人工智能研究院）
