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].
Temporally Abstract Partial Models
Authors: Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, Doina Precup
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Additionally, we empirically demonstrate the ability to learn both affordances and partial option models online resulting in improved sample efficiency and planning time in the Taxi domain.Empirically, we demonstrate end-to-end learning of affordances and partial option models, showcasing significant improvement in final performance and sample efficiency when used for planning in the Taxi domain (Sec. 5). |
| Researcher Affiliation | Collaboration | Khimya Khetarpal 1,2, Zafarali Ahmed 3, Gheorghe Comanici 3, Doina Precup1,2,3 1Mc Gill University, 2Mila, 3Deep Mind |
| Pseudocode | Yes | Affordances can be incorporated into planning by only considering state-option pairs in the affordance set (See Algorithm 1 in the Appendix). |
| Open Source Code | No | The paper states in a footnote: 'We will provide the source code for our empirical analysis here.' However, no active link or specific repository information is provided within the paper. |
| Open Datasets | Yes | Environment. We consider the 5x5 Taxi domain (Dietterich, 2000). |
| Dataset Splits | No | The paper mentions 'train', 'validation', and 'test' in the context of data for experiments, but it does not provide specific details on how the dataset was split (e.g., percentages, sample counts, or specific predefined splits with citations). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions using the 'Launchpad framework (Yang et al., 2021)' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper describes the general experimental pipeline, including data collection and model learning processes, but it does not provide specific hyperparameter values or detailed training configurations (e.g., learning rates, batch sizes, optimizer settings) in the main text. |