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..
Learning Safe Policies with Expert Guidance
Authors: Jessie Huang, Fa Wu, Doina Precup, Yang Cai
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate the behavior of our algorithm in both discrete and continuous problems. |
| Researcher Affiliation | Collaboration | Jessie Huang1 Fa Wu12 Doina Precup1 Yang Cai1 1School of Computer Science, Mc Gill University 2Zhejiang Demetics Medical Technology |
| Pseudocode | Yes | Algorithm 1 Separation Oracle SOR for the reward polytope PR; Algorithm 2 Separation Oracle for the feasible (ยต, z) in LP 1; Algorithm 3 FPL Maxmin Learning |
| Open Source Code | No | The paper does not provide any explicit statements about open-source code availability or links to code repositories for the described methodology. |
| Open Datasets | Yes | Our next experiments are based on the classic control task of cartpole and the environment provided by Open AI Gym [6]. |
| Dataset Splits | No | The paper does not specify explicit training, validation, or test dataset splits in terms of percentages or sample counts. It describes using a 'small (10x10) demonstration gridworld' for expert policy generation and then testing in a 'much larger size (50x50)', but no specific splits are given. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU/CPU models, memory, or specific computing environments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | In the following experiment we set = 0.5 which de๏ฌnes PR and captures how close to optimal the expert is. |