Enhanced-FQL($λ$), an Efficient and Interpretable RL with novel Fuzzy Eligibility Traces and Segmented Experience Replay
This work addresses the need for efficient and interpretable reinforcement learning in safety-critical applications, though it is incremental as it builds on existing fuzzy Q-learning methods.
The paper tackles the problem of reinforcement learning for continuous control by proposing Enhanced-FQL($λ$), which integrates fuzzy eligibility traces and segmented experience replay to achieve superior sample efficiency and reduced variance compared to baselines, with substantially lower computational complexity than deep RL alternatives.
This paper introduces a fuzzy reinforcement learning framework, Enhanced-FQL($λ$), that integrates novel Fuzzified Eligibility Traces (FET) and Segmented Experience Replay (SER) into fuzzy Q-learning with Fuzzified Bellman Equation (FBE) for continuous control tasks. 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. Extensive evaluations in continuous control domains demonstrate that Enhanced-FQL($λ$) achieves superior sample efficiency and reduced variance compared to n-step fuzzy TD and fuzzy SARSA($λ$) baselines, while maintaining substantially lower computational complexity than deep RL alternatives such as DDPG. The framework's inherent interpretability, combined with its computational efficiency and theoretical convergence guarantees, makes it particularly suitable for safety-critical applications where transparency and resource constraints are essential.