Interpretable Control by Reinforcement Learning
This addresses the need for interpretable AI in control systems, though it is incremental as it applies existing RL methods to interpretability.
The paper tackled the problem of generating human-interpretable policies for control tasks like cart-pole balancing using reinforcement learning, achieving successful balancing with compact fuzzy controllers and algebraic equations, including a real-world hardware demonstration.
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. The experiments show that the novel RL methods are able to automatically generate well-performing policies which are at the same time human-interpretable. Furthermore, one of the methods is applied to automatically learn an equation-based policy for a hardware cart-pole demonstrator by using only human-player-generated batch data. The solution generated in the first attempt already represents a successful balancing policy, which demonstrates the methods applicability to real-world problems.