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..
Kernel-Based Reinforcement Learning: A Finite-Time Analysis
Authors: Omar Darwiche Domingues, Pierre Menard, Matteo Pirotta, Emilie Kaufmann, Michal Valko
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically validate our approach in continuous MDPs with sparse rewards. |
| Researcher Affiliation | Collaboration | 1Inria Lille 2Universit e de Lille 3Otto von Guericke University 4Facebook AI Research, Paris 5CNRS 6Deep Mind Paris. |
| Pseudocode | Yes | Algorithm 1 Kernel-UCBVI and Algorithm 2 optimistic Q. |
| Open Source Code | Yes | Implementations of Kernel-UCBVI are available on Git Hub, and use the rlberry library (Domingues et al., 2021). The reference provides the link: https: //github.com/rlberry-py/rlberry |
| Open Datasets | No | The paper describes a custom Grid-World environment (Section 7) but does not provide concrete access information (link, DOI, specific repository, or formal citation for a public dataset) for it. |
| Dataset Splits | No | The paper does not specify explicit training, validation, or test dataset splits or percentages. It operates in an episodic reinforcement learning setting. |
| Hardware Specification | No | The paper does not specify any hardware details like GPU/CPU models, memory, or specific computing infrastructure used for the experiments. |
| Software Dependencies | No | The paper mentions "rlberry library" but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | Yes | We used the Euclidean distance and the Gaussian kernel with a fixed bandwidth σ = 0.025, matching the granularity of the uniform discretization used by some of the baselines. We ran the algorithms for 5 x 10^4 episodes |