Human Behavior Identification for Linear Systems in Adversarial Environments by Adaptive Inverse Reinforcement Learning
研究了对抗环境中线性人机系统的人类行为识别问题，通过将人类建模为最优控制器、环境建模为对手，将问题转化为逆强化学习，并提出了无需持续激励和测量人类控制输入的自适应方法。
This article is concerned with the human behavior identification problem for linear human-in-the-loop (HiTL) systems in adversarial environments. By modeling the human as an optimal controller that minimizes his/her individual cost function and the adversarial environment as an opponent to maximize the cost function, the HiTL system is formulated as a linear-quadratic zero-sum differential game that consists of two players that are the human and adversarial environment. Then, the human behavior identification is transformed to an inverse reinforcement learning (IRL) problem. Accordingly, the main works carried out in this article can be summarized as follows: 1) an integral concurrent learning (ICL) law is proposed to estimate the feedback matrix of the human and 2) based on the estimated feedback matrix, the weighting matrices in human cost function are retrieved by minimizing a residual. The main focus of the developed human behavior identification method is to remove the persisting excitation constraint and the demand for measuring the control input of humans that are universally required in existing online learning approaches. Finally, the results of simulation and experiment on the lane keeping scenario of a vehicle verify the validity of the proposed adaptive-IRL-based human behavior identification strategy.