# A Genetic Fuzzy System for Interpretable and Parsimonious Reinforcement Learning Policies

arXiv: https://arxiv.org/abs/2305.09922 · HTML: https://mlsanity.com/paper/2305.09922/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 26 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 1 of 1)

- [XCS Classifier System with Experience Replay](https://mlsanity.com/paper/2002.05628/) (2020)

## Built on by (most cited first, 2 of 2)

- [Fuzzy-UCS Revisited: Self-Adaptation of Rule Representations in Michigan-Style Learning Fuzzy-Classifier Systems](https://mlsanity.com/paper/2505.06017/) (2025)
- [A Class Inference Scheme With Dempster-Shafer Theory for Learning Fuzzy-Classifier Systems](https://mlsanity.com/paper/2506.03588/) (2025)

## Abstract (arXiv)

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.