A Self-Organizing Neuro-Fuzzy Q-Network: Systematic Design with Offline Hybrid Learning

In this paper, we propose a systematic design process for automatically generating self-organizing neuro-fuzzy Q-networks by leveraging unsupervised learning and an offline, model-free fuzzy reinforcement learning algorithm called Fuzzy Conservative Q-learning (FCQL). Our FCQL offers more effective and interpretable policies than deep neural networks, facilitating human-in-the-loop design and explainability.