Novelty Search in Representational Space for Sample Efficient Exploration
Authors: Ruo Yu Tao, Vincent Francois-Lavet, Joelle Pineau
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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. |