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In this paper, we propose the Model Reference Adaptive Control &Reinforcement Learning (MRAC-RL) approach to developing online policies forsystems in which modeling errors occur in real-time. Although reinforcementlearning (RL) algorithms have been successfully used to develop controlpolicies for dynamical systems, discrepancies between simulated dynamics andthe true target dynamics can cause trained policies to fail to generalize andadapt appropriately when deployed in the real-world. The MRAC-RL frameworkgenerates online policies by utilizing an inner-loop adaptive controllertogether with a simulation-trained outer-loop RL policy. This structure allowsMRAC-RL to adapt and operate effectively in a target environment, even whenparametric uncertainties exists. We propose a set of novel MRAC algorithms,apply them to a class of nonlinear systems, derive the associated control laws,provide stability guarantees for the resulting closed-loop system, and showthat the adaptive tracking objective is achieved. Using a simulation study ofan automated quadrotor landing task, we demonstrate that the MRAC-RL approachimproves upon state-of-the-art RL algorithms and techniques through thegeneration of online policies.

Online Policies for Real-Time Control Using MRAC-RL