Active Learning of Parameterized Skills
Authors: Bruno Da Silva, George Konidaris, Andrew Barto
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate it on a non-linear simulated catapult control problem over arbitrarily mountainous terrains. |
| Researcher Affiliation | Academia | Bruno Castro da Silva BSILVA@CS.UMASS.EDU School of Computer Science, University of Massachusetts Amherst, MA 01003 George Konidaris GDK@CSAIL.MIT.EDU Computer Science and Artiļ¬cial Intelligence Lab, MIT, Cambridge, MA 02139 Andrew Barto BARTO@CS.UMASS.EDU School of Computer Science, University of Massachusetts Amherst, MA 01003 |
| Pseudocode | No | The paper includes mathematical equations and derivations but no pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code will be made available at http://bitbucket. org/bsilvapoa/active_paramskill. |
| Open Datasets | No | The paper uses a simulated environment with randomly generated terrains and does not refer to a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper describes evaluating skill performance on "a set of novel tasks" and averaging over "50 randomly generated terrains" but does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software names with version numbers. |
| Experiment Setup | No | The paper describes the experimental setup in terms of the catapult domain and evaluation metrics but does not provide specific hyperparameters, training configurations, or system-level settings. |