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..
Novelty Search in Representational Space for Sample Efficient Exploration
Authors: Ruo Yu Tao, Vincent Francois-Lavet, Joelle Pineau
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our approach on a number of maze tasks, as well as a control problem and show that our exploration approach is more sample-efficient compared to strong baselines. |
| Researcher Affiliation | Academia | Ruo Yu Tao1, 2, *, Vincent Franc ois-Lavet1, 2, Joelle Pineau1, 2 1 Mc Gill University 2 Mila, Quebec Artificial Intelligence Institute |
| Pseudocode | Yes | Algorithm 1: The Novelty Search algorithm in abstract representational space. |
| Open Source Code | Yes | The code with all experiments is available 1. 1https://github.com/taodav/nsrs |
| Open Datasets | No | The paper describes experiments on environments like 'Acrobot (Brockman et al., 2016)' and 'multi-step maze environment' which are either standard environments or custom-built, but it does not provide explicit access information (link, DOI, formal citation) to any publicly available *dataset* used for training. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or citations to predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Pycolab game engine (Stepleton, 2017)' but does not provide specific version numbers for software dependencies or libraries used to replicate the experiment. |
| Experiment Setup | Yes | We discuss all environment-specific hyperparameters in Appendix J. |