A Genetic Fuzzy System for Interpretable and Parsimonious Reinforcement Learning Policies
| Autor: | Will N. Browne, Jordan T. Bishop, Marcus Gallagher |
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| Rok vydání: | 2023 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Fuzzy rule Cooperative coevolution Computer science business.industry Computer Science - Artificial Intelligence Scale (chemistry) Computer Science - Neural and Evolutionary Computing Fuzzy control system Fuzzy logic Machine Learning (cs.LG) Artificial Intelligence (cs.AI) Genetic fuzzy systems Classifier (linguistics) Reinforcement learning Artificial intelligence Neural and Evolutionary Computing (cs.NE) business |
| Zdroj: | GECCO Companion |
| DOI: | 10.48550/arxiv.2305.09922 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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