An application of explainability methods in reinforcement learning

Abstract

La popularidad de los métodos explicativos está aumentando en el contexto de la Inteligencia Artificial y consiste en dar explicaciones inteligibles a modelos complejos. Recientemente, en el contexto del Aprendizaje Reforzado (RL) han aparecido muchos trabajos teóricos que intentan explicar el comportamiento de agentes entrenados, con predicados y usando representaciones del comportamiento observado en forma de grafo MDP. Nosotros aplicaremos este método a un entorno de RL y crearemos una política que intentará demostrar su correcto funcionamiento.

The popularity of explainability methods is increasing in the Artificial Intelligence context and consists of giving intelligible explanations to complex models. Recently, in the Reinforcement Learning (RL) context several theoretical works have appeared that try to explain the behavior of trained agents, with predicates and using representations of the observed behavior in a graph form (MDP). We will apply this method to an RL environment and create a policy that will try to prove its correct functioning.