Improving performance using robust recurrent reinforcement learning control

A recurrent neural network (RNN) is used inside the feedback loop to improve closed-loop tracking performance of a nonlinear plant. An actor-critic reinforcement learning algorithm is used to optimize the RNN actor as the plant operates in real-time. Integral Quadratic Constraints (IQCs) are used to guarantee robust stability of the closed-loop system as the RNN actor learns online. Using IQCs, we can deal with both model uncertainty and the nonlinear elements of the RNN actor in a single unified framework. The RNN actor provides dynamic capabilities that a feed-forward neural network could not, which results in a robust recurrent reinforcement learning controller that is able to provide enhanced nonlinear dynamic control.