Robust Learning from Demonstration Techniques and Tools
Authors: William Curran
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have shown the speed and robustness of this approach when combined with standard Q-Learning in the Mountaincar 3D (Figure 1) domain using good and bad demonstrations. |
| Researcher Affiliation | Academia | William Curran Oregon State University Corvallis, Oregon curranw@oregonstate.edu |
| Pseudocode | No | The paper describes algorithms and methods but does not include any explicit pseudocode blocks or algorithm figures. |
| Open Source Code | No | The paper does not state that the code for the developed methods (DRRL, IDRRL, or the movie-reel interface) is open-source, nor does it provide any links to a code repository. |
| Open Datasets | No | The paper mentions experiments in the |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or citations to predefined splits) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper mentions the PR2 robot's physical specifications but does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments or training models. |
| Software Dependencies | No | The paper mentions software like RViz, ROS, Q-Learning, Oculus Rift, and Razer Hydra but does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings. |