Interpretable Control by Reinforcement Learning

Daniel Hein, Steffen Limmer, Thomas A. Runkler

In this paper, three recently introduced reinforcement learning (RL) methods are used to generate human-interpretable policies for the cart-pole balancing benchmark. The novel RL m…