  Despite the numerous advances, reinforcement learning remains away from
widespread acceptance for autonomous controller design as compared to classical
methods due to lack of ability to effectively tackle the reality gap. The
reliance on absolute or deterministic reward as a metric for optimization
process renders reinforcement learning highly susceptible to changes in problem
dynamics. We introduce a novel framework that effectively quantizes the
uncertainty of the design space and induces robustness in controllers by
switching to a reliability-based optimization routine. The data efficiency of
the method is maintained to match reward based optimization methods by
employing a model-based approach. We prove the stability of learned
neuro-controllers in both static and dynamic environments on classical
reinforcement learning tasks such as Cart Pole balancing and Inverted Pendulum.

Machine Learning;Systems and Control;Machine Learning