Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Reward-Free Kernel-Based Reinforcement Learning
Authors: Sattar Vakili, Farhang Nabiei, Da-Shan Shiu, Alberto Bernacchia
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We consider a broad range of kernel-based function approximations, including non-smooth kernels, and propose an algorithm based on adaptive domain partitioning. We show that our algorithm achieves order-optimal sample complexity for a large class of common kernels, which includes Mat ern and Neural Tangent kernels. |
| Researcher Affiliation | Industry | 1Media Tek Research. Correspondence to: Sattar Vakili <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Exploration Phase, Algorithm 2 Planning Phase |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on sample complexity analysis for a reward-free RL framework, rather than conducting empirical studies on a specific dataset. Therefore, it does not mention dataset availability for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on specific datasets, hence no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and sample complexity analysis. It does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and describes algorithms and mathematical analysis. It does not mention any specific software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and sample complexity analysis. It does not describe any specific experimental setup, hyperparameters, or training configurations. |