Model-Based Relative Entropy Stochastic Search
Authors: Abbas Abdolmaleki, Rudolf Lioutikov, Jan R. Peters, Nuno Lau, Luis Pualo Reis, Gerhard Neumann
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | 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. |