Robust reinforcement learning control

Robust control theory is used to design stable controllers in the presence of uncertainties. By replacing nonlinear and time-varying aspects of a neural network with uncertainties, a robust reinforcement learning procedure results that is guaranteed to remain stable even as the neural network is being trained. The behavior of this procedure is demonstrated and analyzed on a simple control task. Reinforcement learning with and without robust constraints results in the same control performance, but at intermediate stages the system without robust constraints may go through a period of unstable behavior that is avoided when the robust constraints are included.