Local Bayesian Optimization of Motor Skills
Authors: Riad Akrour, Dmitry Sorokin, Jan Peters, Gerhard Neumann
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our algorithm on several benchmark objective functions as well as a continuous robotic task in which an informative prior is obtained by imitation learning. and 4. Experiments |
| Researcher Affiliation | Academia | 1CLAS/IAS, TU Darmstadt, Darmstadt, Germany 2Max Planck Institute for Intelligent Systems, T ubingen, Germany 3LCAS, University of Lincoln, Lincoln, United Kingdom. |
| Pseudocode | Yes | Algorithm 1 Local Bayesian Optimization of Motor Skills |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of its source code. |
| Open Datasets | Yes | We then conduct a comparison to the state-of-the-art on the COmparing COntinuous optimisers (COCO) testbed on the 20 functions f5 to f24 (we refer the reader to http://coco.gforge.inria.fr/ for an illustration and the mathematical definition of each function). |
| Dataset Splits | No | The paper uses benchmark functions like the COCO testbed and a robotic task, but it does not specify explicit training, validation, or test dataset splits for these. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | We rely on the GPStuff library (Vanhatalo et al., 2013) for the GP implementation and the posterior sampling of hyper-parameters. We use the Bayes Opt library (Martinez-Cantin, 2014)... (no version numbers specified). |
| Experiment Setup | Yes | In all but the last experiment ϵ = β = .05 while for the robotics experiment with an initial solution learned by imitation learning we set a more aggressive step size and entropy reduction ϵ = β = 1. We choose to use an equality constraint for the entropy reduction for both algorithms. As a result, both L-Bayes Opt and MORE will have the same entropy at every iteration and any difference in performance will be attributed to a better location of the mean, adaptation of the covariance matrix or sampling procedure rather than a faster reduction in exploration. In all but the last experiment ϵ = β = .05 while for the robotics experiment with an initial solution learned by imitation learning we set a more aggressive step size and entropy reduction ϵ = β = 1. And will sample ten points per iteration. and for the local stochastic search algorithms we set the initial distribution to π0 = N(0, 3I). |