Enhanced-FQL(λ)：新規ファジィ適格度トレースと分割経験再生による効率的で解釈可能な強化学習
Enhanced-FQL($\lambda$), an Efficient and Interpretable RL with novel Fuzzy Eligibility Traces and Segmented Experience Replay
ファジィルールベースを用いた強化学習に、ファジィ化ベルマン方程式と適格度トレース、分割経験再生を組み合わせ、連続制御タスクでサンプル効率と安定性を向上させた手法を提案。
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著者: Mohsen Jalaeian-Farimani, Xiong Xiong, Luca Bascetta
分類: cs.LG, cs.AI, cs.RO, cs.SY, eess.SY, math.OC
原文アブストラクト
This paper introduces a fuzzy reinforcement learning framework, Enhanced-FQL($\lambda$), that integrates novel Fuzzified Eligibility Traces (FET) and Segmented Experience Replay (SER) into fuzzy Q-learning with the Fuzzified Bellman Equation (FBE) for continuous control. The proposed approach employs an interpretable fuzzy rule base instead of complex neural architectures, while maintaining competitive performance through two key innovations: a fuzzified Bellman equation with eligibility traces for stable multi-step credit assignment, and a memory-efficient segment-based experience replay mechanism for enhanced sample efficiency. Theoretical analysis proves the proposed method convergence under standard assumptions. On the Cart--Pole benchmark, Enhanced-FQL($\lambda$) improves sample efficiency and reduces variance relative to $n$-step fuzzy TD and fuzzy SARSA($\lambda$), while remaining competitive with the tested DDPG baseline. These results support the proposed framework as an interpretable and computationally compact alternative for moderate-scale continuous control problems.
