Autonomous Model Learning for Reinforcement Learning

Stochastic modeling is an excellent way of capturing system dynamics so that alternative control strategies can be evaluated and compared. I will discuss attributes that make some problems amenable to autonomous learning of system dynamics. I will then present recent advances in my lab concerning the design of learning algorithms with formal learning-time guarantees in the "KWIK" (knows what it knows) formalism along with their implementation on robotic and software control problems.