Alternating Optimisation and Quadrature for Robust Control

Understand

Bayesian optimisation has been successfully applied to a variety of reinforcement learning problems.

• However, the traditional approach for learning optimal policies in simulators does not utilise the opportunity to improve learning by adjusting certain environment variables: state features that are unobservable and randomly determined by the environment in a physical setting but are controllable in a simulator.
• This paper considers the problem of finding a robust policy while taking into account the impact of environment variables.
• We present Alternating Optimisation and Quadrature (ALOQ), which uses Bayesian optimisation and Bayesian quadrature to address such settings.