Active Imitation Learning of Hierarchical Policies
Authors: Mandana Hamidi, Prasad Tadepalli, Robby Goetschalckx, Alan Fern
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results in five different domains exhibit successful learning using fewer queries than a variety of alternatives. |
| Researcher Affiliation | Academia | Mandana Hamidi, Prasad Tadepalli, Robby Goetschalckx, Alan Fern School of EECS, Oregon State University, Corvallis, OR, USA |
| Pseudocode | Yes | Algorithm 1: Learning Hierarchical Policies |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is open or publicly available. |
| Open Datasets | No | In each domain, we collected a set of training and test trajectories from a hand-coded hierarchical policy that follows the hierarchical structure shown in Figure 1. The paper does not provide specific access information (link, DOI, formal citation) for a publicly available dataset. |
| Dataset Splits | No | The paper mentions "training and test trajectories" but does not specify a distinct validation split or set. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., specific CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions TILDE as a relational decision tree learning algorithm but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper describes general experimental settings like averaging over 10 runs and using 70 sampled parse trees, but does not provide concrete numerical values for hyperparameters (e.g., learning rate, batch size, epochs) or detailed system-level training configurations. |