论文标题
基于在线观察者的逆增强学习
Online Observer-Based Inverse Reinforcement Learning
论文作者
论文摘要
在本文中，通过施放IRL问题，对于具有二次成本函数的线性系统，作为一个状态估计问题来开发出一种新颖的输出反馈反馈逆增强学习（IRL）问题。开发了两种基于观察者的IRL基于观察者的技术，包括一种新型的观察者方法，该方法可以通过历史堆栈重新使用先前的状态估计。在适当的激发条件下建立了融合和鲁棒性的理论保证。模拟证明了在嘈杂和无噪声测量结果下发达的观察者和过滤器的性能。
In this paper, a novel approach to the output-feedback inverse reinforcement learning (IRL) problem is developed by casting the IRL problem, for linear systems with quadratic cost functions, as a state estimation problem. Two observer-based techniques for IRL are developed, including a novel observer method that re-uses previous state estimates via history stacks. Theoretical guarantees for convergence and robustness are established under appropriate excitation conditions. Simulations demonstrate the performance of the developed observers and filters under noisy and noise-free measurements.
