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..
Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning
Authors: Yonathan Efroni, Gal Dalal, Bruno Scherrer, Shie Mannor
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we study multiple-step greedy algorithms in more practical setups. We begin by highlighting a counter-intuitive difficulty... we formulate and analyze online and approximate algorithms that use such a multi-step greedy operator. and a next indisputable step would be to empirically evaluate implementations of the algorithms presented here. |
| Researcher Affiliation | Academia | Yonathan Efroni EMAIL Gal Dalal EMAIL Bruno Scherrer EMAIL Shie Mannor EMAIL Department of Electrical Engineering, Technion, Israel Institute of Technology INRIA, Villers les Nancy, France |
| Pseudocode | Yes | Algorithm 1 Two-Timescale Online κ-Policy-Iteration, Algorithm 2 κ-API, Algorithm 3 κ-PSDP |
| Open Source Code | No | The paper does not contain any statement about making its source code available. The discussion section states: "Lastly, a next indisputable step would be to empirically evaluate implementations of the algorithms presented here." |
| Open Datasets | No | The paper is theoretical and does not use datasets. It defines an MDP framework but does not mention specific training data. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with datasets, thus no dataset splits are discussed. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments, thus no software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not report on experiments, thus no experimental setup details like hyperparameters or training settings are provided. |