A Genetic Fuzzy System for Interpretable and Parsimonious Reinforcement Learning Policies
This work addresses the need for more interpretable and scalable reinforcement learning policies, though it is incremental as it builds on existing genetic fuzzy systems.
The paper tackled the problem of evolving large, uninterpretable rule bases in reinforcement learning by proposing a Pittsburgh Genetic Fuzzy System that balances policy performance and complexity, achieving interpretable, high-performing policies with minimal rules in the Mountain Car environment.
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.