Local policy search with Bayesian optimization
Authors: Sarah Müller, Alexander von Rohr, Sebastian Trimpe
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The comparison reveals improved sample complexity and reduced variance in extensive empirical evaluations on synthetic objectives. Further, we highlight the benefits of active sampling on popular RL benchmarks. |
| Researcher Affiliation | Collaboration | 1Max Planck Institute for Intelligent Systems, Stuttgart, Germany 2Institute for Data Science in Mechanical Engineering, RWTH Aachen University, Germany 3IAV Gmb H, Gifhorn, Germany 4 Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany |
| Pseudocode | Yes | Algorithm 1 GIBO |
| Open Source Code | Yes | All data and source code necessary to reproduce the results are published at https://github.com/sarmueller/gibo. |
| Open Datasets | Yes | Lastly, we evaluate the performance of GIBO on classic control and Mu Jo Co tasks included in the Open AI Gym [35, 36]. |
| Dataset Splits | No | The paper describes using synthetic functions for 'within-model comparison' and RL environments (Gym and MuJoCo) for evaluation, showing 'training curves'. However, it does not provide explicit numerical train/validation/test splits (e.g., percentages or sample counts) or specify how data was partitioned for these purposes beyond general evaluation settings. |
| Hardware Specification | No | The paper does not specify any particular hardware components such as GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper mentions software like 'Bo Torch' and 'Gpytorch' but does not specify their version numbers or any other software dependencies with version details. |
| Experiment Setup | Yes | All algorithms were started in the middle of the domain x0 = [0.5]d and had a limited budget of 300 noised function evaluations. The noise was Gaussian distributed with standard deviation σ = 0.1. |