  Reinforcement learning (RL) algorithms have been successfully used to develop
control policies for dynamical systems. For many such systems, these policies
are trained in a simulated environment. Due to discrepancies between the
simulated model and the true system dynamics, RL trained policies often fail to
generalize and adapt appropriately when deployed in the real-world environment.
Current research in bridging this sim-to-real gap has largely focused on
improvements in simulation design and on the development of improved and
specialized RL algorithms for robust control policy generation. In this paper
we apply principles from adaptive control and system identification to develop
the model-reference adaptive control & reinforcement learning (MRAC-RL)
framework. We propose a set of novel MRAC algorithms applicable to a broad
range of linear and nonlinear systems, and derive the associated control laws.
The MRAC-RL framework utilizes an inner-loop adaptive controller that allows a
simulation-trained outer-loop policy to adapt and operate effectively in a test
environment, even when parametric model uncertainty exists. We demonstrate that
the MRAC-RL approach improves upon state-of-the-art RL algorithms in developing
control policies that can be applied to systems with modeling errors.
