mlsanityData to 14 Oct 2026

Enhanced-FQL(λ), an Efficient and Interpretable RL with novel Fuzzy Eligibility Traces and Segmented Experience Replay

arXiv 2601.04392 ↗First posted 7 Jan 2026cs.LG cs.AI cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL theory and sample complexity

Abstract (arXiv)

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 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(λ) improves sample efficiency and reduces variance relative to n-step fuzzy TD and fuzzy SARSA(λ), 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 19 references that are in the map. First posted on arXiv 7 Jan 2026.

Builds on

5 of 19 references.

References · 5

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  3. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  4. 2017Rainbow: Combining Improvements in Deep Reinforcement Learningcited by 786
  5. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785

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  1. 2026Fuz-RL: A Fuzzy-Guided Robust Framework for Safe Reinforcement Learning under Uncertainty
  2. 2026Fuzzy Logic Theory-based Adaptive Reward Shaping for Robust Reinforcement Learning (FARS)
  3. 2024Fuzzy Logic Guided Reward Function Variation: An Oracle for Testing Reinforcement Learning Programs
  4. 2026Distilling Deep Reinforcement Learning into Interpretable Fuzzy Rules: An Explainable AI Framework
  5. 2016Particle Swarm Optimization for Generating Interpretable Fuzzy Reinforcement Learning Policies
  6. 2025Continuous Q-Score Matching: Diffusion Guided Reinforcement Learning for Continuous-Time Control
  7. 2024ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning
  8. 2018Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming