A Genetic Fuzzy System for Interpretable and Parsimonious Reinforcement Learning Policies

05/17/2023

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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.

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A Genetic Fuzzy System for Interpretable and Parsimonious Reinforcement Learning Policies

Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming

Distilling Deep RL Models Into Interpretable Neuro-Fuzzy Systems

Pittsburgh Learning Classifier Systems for Explainable Reinforcement Learning: Comparing with XCS

Particle Swarm Optimization for Generating Interpretable Fuzzy Reinforcement Learning Policies

Boolean Decision Rules for Reinforcement Learning Policy Summarisation

Efficient Use of heuristics for accelerating XCS-based Policy Learning in Markov Games

Efficient Hierarchical Storage Management Framework Empowered by Reinforcement Learning

A Genetic Fuzzy System for Interpretable and Parsimonious Reinforcement Learning Policies

Related Research

Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming

Distilling Deep RL Models Into Interpretable Neuro-Fuzzy Systems

Pittsburgh Learning Classifier Systems for Explainable Reinforcement Learning: Comparing with XCS

Particle Swarm Optimization for Generating Interpretable Fuzzy Reinforcement Learning Policies

Boolean Decision Rules for Reinforcement Learning Policy Summarisation

Efficient Use of heuristics for accelerating XCS-based Policy Learning in Markov Games

Efficient Hierarchical Storage Management Framework Empowered by Reinforcement Learning