Cover Image

Balancing Interpretability and Performance in Reinforcement Learning: An Adaptive Spectral Based Linear Approach

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yi, Qianxin, Lin, Shao-Bo, Fan, Jun, Wang, Yao
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Kernel-Based Distributed Q-Learning: A Scalable Reinforcement Learning Approach for Dynamic Treatment Regimes
by: Wang, Di, et al.
Published: (2023)

Tensor Completion Leveraging Graph Information: A Dynamic Regularization Approach with Statistical Guarantees
by: Wang, Kaidong, et al.
Published: (2023)

Towards Accurate and Interpretable Time-series Forecasting: A Polynomial Learning Approach
by: Liu, Bo, et al.
Published: (2026)

Striking the Perfect Balance: Preserving Privacy While Boosting Utility in Collaborative Medical Prediction Platforms
by: Lin, Shao-Bo, et al.
Published: (2025)

Efficient Generalized Low-Rank Tensor Contextual Bandits
by: Yi, Qianxin, et al.
Published: (2023)

Focal Reward: Balanced Reinforcement Learning under Rubric-Based Rewards
by: Huang, Yu, et al.
Published: (2026)

Geometry-Aware Approaches for Balancing Performance and Theoretical Guarantees in Linear Bandits
by: Luo, Yuwei, et al.
Published: (2023)

Non-Rival Data as Rival Products: An Encapsulation-Forging Approach for Data Synthesis
by: Wang, Kaidong, et al.
Published: (2025)

Weighted Spectral Filters for Kernel Interpolation on Spheres: Estimates of Prediction Accuracy for Noisy Data
by: Liu, Xiaotong, et al.
Published: (2024)

Integral Operator Approaches for Scattered Data Fitting on Spheres
by: Lin, Shao-Bo
Published: (2024)

ETTRL: Balancing Exploration and Exploitation in LLM Test-Time Reinforcement Learning Via Entropy Mechanism
by: Liu, Jia, et al.
Published: (2025)

Spectral Representation-based Reinforcement Learning
by: Gao, Chenxiao, et al.
Published: (2025)

Diffusion Spectral Representation for Reinforcement Learning
by: Shribak, Dmitry, et al.
Published: (2024)

Interpretable Reinforcement Learning for Load Balancing using Kolmogorov-Arnold Networks
by: Singh, Kamal, et al.
Published: (2025)

Feature Qualification by Deep Nets: A Constructive Approach
by: Cao, Feilong, et al.
Published: (2025)

Beyond Cross-Validation: Adaptive Parameter Selection for Kernel-Based Gradient Descents
by: Liu, Xiaotong, et al.
Published: (2026)

Deep Learning-Based Prediction of Suspension Dynamics Performance in Multi-Axle Vehicles
by: Lin, Kai Chun, et al.
Published: (2024)

Foster Adaptivity and Balance in Learning with Noisy Labels
by: Sheng, Mengmeng, et al.
Published: (2024)

AMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agents
by: Grigsby, Jake, et al.
Published: (2023)

Hybrid Reinforcement Learning Breaks Sample Size Barriers in Linear MDPs
by: Tan, Kevin, et al.
Published: (2024)

Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretability
by: Zheng, Mingyi, et al.
Published: (2025)

Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning
by: Shao, Zhiyu, et al.
Published: (2024)

MechRL: Reinforcement Learning Agents Perform Circuit Discovery for Mechanistic Interpretability
by: Khadka, Barsat
Published: (2026)

Taming OOD Actions for Offline Reinforcement Learning: An Advantage-Based Approach
by: Chen, Xuyang, et al.
Published: (2025)

Learning Hyperplane Tree: A Piecewise Linear and Fully Interpretable Decision-making Framework
by: Li, Hongyi, et al.
Published: (2025)

Active Learning For Contextual Linear Optimization: A Margin-Based Approach
by: Liu, Mo, et al.
Published: (2023)

Adaptive Data Exploitation in Deep Reinforcement Learning
by: Yuan, Mingqi, et al.
Published: (2025)

Attention-Based Offline Reinforcement Learning and Clustering for Interpretable Sepsis Treatment
by: Kumar, Punit, et al.
Published: (2026)

Balancing Classification and Calibration Performance in Decision-Making LLMs via Calibration Aware Reinforcement Learning
by: Yaldiz, Duygu Nur, et al.
Published: (2026)

The Interpretability of Codebooks in Model-Based Reinforcement Learning is Limited
by: Eaton, Kenneth, et al.
Published: (2024)

A Survey on Neural Architecture Search Based on Reinforcement Learning
by: Shao, Wenzhu
Published: (2024)

Interpretable Operator Learning for Inverse Problems via Adaptive Spectral Filtering: Convergence and Discretization Invariance
by: Dong, Hang-Cheng, et al.
Published: (2026)

Succeed or Learn Slowly: Sample Efficient Off-Policy Reinforcement Learning for Mobile App Control
by: Papoudakis, Georgios, et al.
Published: (2025)

Interpretable Learning Dynamics in Unsupervised Reinforcement Learning
by: Pandey, Shashwat
Published: (2025)

FinXplore: An Adaptive Deep Reinforcement Learning Framework for Balancing and Discovering Investment Opportunities
by: Choudhary, Himanshu, et al.
Published: (2025)

Adaptive Scaling of Policy Constraints for Offline Reinforcement Learning
by: Jing, Tan, et al.
Published: (2025)

Retentive Decision Transformer with Adaptive Masking for Reinforcement Learning based Recommendation Systems
by: Wang, Siyu, et al.
Published: (2024)

Interpreting Reinforcement Learning Agents with Susceptibilities
by: Elliott, Chris, et al.
Published: (2026)

Mechanistic Interpretability of Reinforcement Learning Agents
by: Trim, Tristan, et al.
Published: (2024)

Class-Balanced and Reinforced Active Learning on Graphs
by: Yu, Chengcheng, et al.
Published: (2024)