Interpretable Control by Reinforcement Learning

Understand

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 methods learn human-interpretable policies in the form of compact fuzzy controllers and simple algebraic equations.
• The representations as well as the achieved control performances are compared with two classical controller design methods and three non-interpretable RL methods.
• All eight methods utilize the same previously generated data batch and produce their controller offline - without interaction with the real benchmark dynamics.