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..
Efficient Planning in Large MDPs with Weak Linear Function Approximation
Authors: Roshan Shariff, Csaba Szepesvari
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We design a randomized algorithm that positively answers the challenge posed above under one extra assumption that the feature vectors of all states lie within the convex hull of the feature vectors of a few selected core states that the algorithm is given. In particular, we show that the query-complexity and runtime of our algorithm is polynomial in the relevant quantities and the number of core states, providing a partial positive answer to the previously open problem of efficient planning in the presence of weak features. |
| Researcher Affiliation | Collaboration | Roshan Shariff University of Alberta & Amii EMAIL Csaba Szepesvári Deep Mind & University of Alberta & Amii EMAIL |
| Pseudocode | Yes | Algorithm 1 Core Sto MP: Stochastic Mirror-Prox for Planning with Core States |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | No | This is a theoretical paper presenting an algorithm and its analysis; it does not describe experiments with datasets. |
| Dataset Splits | No | This is a theoretical paper presenting an algorithm and its analysis; it does not describe experiments with datasets, and thus no dataset splits are provided. |
| Hardware Specification | No | The paper is theoretical and does not report on empirical experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper describes a theoretical algorithm (Core Sto MP) and its mathematical properties but does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not report empirical experiments; therefore, no experimental setup details, such as hyperparameters or training settings, are provided. |