  The objective of this research is to enable safety-critical systems to
simultaneously learn and execute optimal control policies in a safe manner to
achieve complex autonomy. Learning optimal policies via trial and error, i.e.,
traditional reinforcement learning, is difficult to implement in
safety-critical systems, particularly when task restarts are unavailable. Safe
model-based reinforcement learning techniques based on a barrier transformation
have recently been developed to address this problem. However, these methods
rely on full state feedback, limiting their usability in a real-world
environment. In this work, an output-feedback safe model-based reinforcement
learning technique based on a novel barrier-aware dynamic state estimator has
been designed to address this issue. The developed approach facilitates
simultaneous learning and execution of safe control policies for
safety-critical linear systems. Simulation results indicate that barrier
transformation is an effective approach to achieve online reinforcement
learning in safety-critical systems using output feedback.
