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..
Model-Based Relative Entropy Stochastic Search
Authors: Abbas Abdolmaleki, Rudolf Lioutikov, Jan R. Peters, Nuno Lau, Luis Pualo Reis, Gerhard Neumann
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare our method with state of art black-box optimization methods on standard uni-modal and multi-modal optimization functions, on simulated planar robot tasks and a complex robot ball throwing task. The proposed method considerably outperforms the existing approaches. |
| Researcher Affiliation | Academia | 1: IEETA, University of Aveiro, Aveiro, Portugal 2: DSI, University of Minho, Braga, Portugal 3: LIACC, University of Porto, Porto, Portugal 4: IAS, 5: CLAS, TU Darmstadt, Darmstadt, Germany 6: Max Planck Institute for Intelligent Systems, Stuttgart, Germany |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper uses standard optimization test functions (Rosenbrock, Rastrigin) and simulated robot tasks, but does not provide concrete access information (link, DOI, specific citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components and algorithms used (e.g., DMPs, CMA-ES, NES, REPS) but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | In each iteration, we generated 15 new samples. For MORE, REPS and Po WER, we always keep the last L = 150 samples... We used 5 basis functions per degree of freedom for the DMPs... We generated 40 new samples. For MORE, REPS, we always keep the last L = 200 samples. |