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

• URL: http://arxiv.org/abs/2601.04392v1
• Date: Wed, 07 Jan 2026 20:59:18 GMT
• Title: Enhanced-FQL($λ$), an Efficient and Interpretable RL with novel Fuzzy Eligibility Traces and Segmented Experience Replay
• Authors: Mohsen Jalaeian-Farimani,
• Abstract summary: Enhanced-FQL($$) achieves superior sample efficiency and reduced variance compared to n-step fuzzy TD and fuzzyA($$) baselines.<n>The framework's inherent interpretability, combined with its computational efficiency and theoretical convergence guarantees, makes it suitable for safety-critical applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• CVeDRL: An Efficient Code Verifier via Difficulty-aware Reinforcement Learning [57.24524263804788]
  Code verifiers play a critical role in post-verification for LLM-based code generation.<n>Existing supervised fine-tuning methods suffer from data scarcity, high failure rates, and poor inference efficiency.<n>We show that naive RL with only functionality rewards fails to generate effective unit tests for difficult branches and samples.
  arXiv  Detail & Related papers  (2026-01-30T10:33:29Z)
• FIRE: Multi-fidelity Regression with Distribution-conditioned In-context Learning using Tabular Foundation Models [3.8824066002669855]
  Multi-fidelity (MF) regression often operates in regimes of extreme data imbalance.<n>We introduce FIRE, a training-free MF framework.<n>Fire delivers a stronger performance-time trade-off than seven state-of-the-art GP-based or deep learning MF regression methods.
  arXiv  Detail & Related papers  (2026-01-29T22:29:58Z)
• How to Set the Learning Rate for Large-Scale Pre-training? [73.03133634525635]
  We formalize this investigation into two distinct research paradigms: Fitting and Transfer.<n>Within the Fitting Paradigm, we introduce a Scaling Law for search factor, effectively reducing the search complexity from O(n3) to O(n*C_D*C_) via predictive modeling.<n>We extend the principles of $$Transfer to the Mixture of Experts (MoE) architecture, broadening its applicability to encompass model depth, weight decay, and token horizons.
  arXiv  Detail & Related papers  (2026-01-08T15:55:13Z)
• Principled RL for Diffusion LLMs Emerges from a Sequence-Level Perspective [85.06838178922791]
  Reinforcement Learning (RL) has proven highly effective for autoregressive language models.<n>But adapting these methods to diffusion large language models (dLLMs) presents fundamental challenges.<n>We propose a principled RL framework that treats entire sequence generation as a single action and uses the ELBO as a tractable sequence-level likelihood proxy.
  arXiv  Detail & Related papers  (2025-12-03T13:05:32Z)
• CoT-Saliency: Unified Chain-of-Thought Reasoning for Heterogeneous Saliency Tasks [96.64597365827046]
  We present the first unified framework that jointly handles three operationally heterogeneous saliency tasks.<n>We introduce a Chain-of-Thought (CoT) reasoning process in a Vision-Language Model (VLM) to bridge task heterogeneity.<n>We show our model matches or outperforms specialized SOTA methods and strong closed-source VLMs across all tasks.
  arXiv  Detail & Related papers  (2025-11-01T04:37:01Z)
• Anchored Supervised Fine-Tuning [26.17356786243252]
  Post-training of large language models involves a trade-off between supervised fine-tuning and reinforcement learning.<n> Dynamic Fine-Tuning (DFT) recently emerged as a promising middle ground, reweighting SFT objectives with token probabilities.<n>We propose Anchored Supervised Fine-Tuning (ASFT) to augment DFT's reweighting with lightweight KL regularization to preserve tightness while ensuring stability.
  arXiv  Detail & Related papers  (2025-09-28T08:58:12Z)
• EconProver: Towards More Economical Test-Time Scaling for Automated Theorem Proving [64.15371139980802]
  Large Language Models (LLMs) have recently advanced the field of Automated Theorem Proving (ATP)<n>We show that different test-time scaling strategies for ATP models introduce significant computational overhead for inference.<n>We propose two complementary methods that can be integrated into a unified EconRL pipeline for amplified benefits.
  arXiv  Detail & Related papers  (2025-09-16T03:00:13Z)
• Supervised Optimism Correction: Be Confident When LLMs Are Sure [91.7459076316849]
  We establish a novel theoretical connection between supervised fine-tuning and offline reinforcement learning.<n>We show that the widely used beam search method suffers from unacceptable over-optimism.<n>We propose Supervised Optimism Correction, which introduces a simple yet effective auxiliary loss for token-level $Q$-value estimations.
  arXiv  Detail & Related papers  (2025-04-10T07:50:03Z)
• A Multi-Fidelity Control Variate Approach for Policy Gradient Estimation [22.095132833345776]
  reinforcement learning algorithms are impractical for deployment in operational systems or for training with expensive high-fidelity simulations.<n>Lowfidelity simulators can provide useful data for RL training, even if they are too coarse for zero-shot transfer.<n>We propose multi-fidelity policy robotics (Gs) that mixes a small amount of data from the target environment.
  arXiv  Detail & Related papers  (2025-03-07T18:58:23Z)
• f-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimization [9.591164070876689]
  This paper presents a unified optimization framework for fair empirical risk based on f-divergence measures (f-FERM) In addition, our experiments demonstrate the superiority of fairness-accuracy tradeoffs offered by f-FERM for almost all batch sizes. Our extension is based on a distributionally robust optimization reformulation of f-FERM objective under $L_p$ norms as uncertainty sets.
  arXiv  Detail & Related papers  (2023-12-06T03:14:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.