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..
Correlation Priors for Reinforcement Learning
Authors: Bastian Alt, Adrian Šošić, Heinz Koeppl
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the framework on a number of common decision-making related problems, such as imitation learning, subgoal extraction, system identification and Bayesian reinforcement learning. |
| Researcher Affiliation | Academia | Technische Universität Darmstadt EMAIL |
| Pseudocode | No | The paper provides detailed mathematical derivations and descriptions of the variational inference method, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The accompanying code is publicly available via Git.1 https://git.rwth-aachen.de/bcs/correlation_priors_for_rl |
| Open Datasets | No | The paper describes using generated demonstration data sets and data from simulated environments, but does not provide access information or citations for any publicly available or open datasets. |
| Dataset Splits | No | The paper describes evaluating models on observed data and simulated environments, but does not provide specific train/validation/test dataset split information. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For the experiments in the following section, we consider a squared exponential covariance function of the form ( )cc0 = exp [ d(c, c0) 2 /l2], with a covariate distance measure d : C C ! R 0 and a length scale l 2 R 0 adapted to the specific modeling scenario. To capture the underling correlations, we used the Euclidean distance between the grid positions as covariate distance measure d and set l to the maximum occurring distance value. Fig. 2c shows the policy model obtained by averaging the predictive action distributions of M = 100 drawn subgoal configurations. |