A Genetic Fuzzy System for Interpretable and Parsimonious Reinforcement
  Learning Policies
Jordan T. Bishop Marcus Gallagher Will N. Browne
Reinforcement learning (RL) is experiencing a resurgence in research
interest, where Learning Classifier Systems (LCSs) have been applied for many
years. However, traditional Michigan approaches tend to evolve large rule bases
that are difficult to interpret or scale to domains beyond standard mazes. A
Pittsburgh Genetic Fuzzy System (dubbed Fuzzy MoCoCo) is proposed that utilises
both multiobjective and cooperative coevolutionary mechanisms to evolve fuzzy
rule-based policies for RL environments. Multiobjectivity in the system is
concerned with policy performance vs. complexity. The continuous state RL
environment Mountain Car is used as a testing bed for the proposed system.
Results show the system is able to effectively explore the trade-off between
policy performance and complexity, and learn interpretable, high-performing
policies that use as few rules as possible.