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 [1].
Active Learning of Parameterized Skills
Authors: Bruno Da Silva, George Konidaris, Andrew Barto
ICML 2014 | Venue PDF | 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 EMAIL School of Computer Science, University of Massachusetts Amherst, MA 01003 George Konidaris EMAIL Computer Science and Arti๏ฌcial Intelligence Lab, MIT, Cambridge, MA 02139 Andrew Barto EMAIL 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. |