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].
Near Optimal Behavior via Approximate State Abstraction
Authors: David Abel, David Hershkowitz, Michael Littman
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present theoretical guarantees of the quality of behaviors derived from four types of approximate abstractions. Additionally, we empirically demonstrate that approximate abstractions lead to reduction in task complexity and bounded loss of optimality of behavior in a variety of environments. |
| Researcher Affiliation | Academia | David Abel DAVID EMAIL D. Ellis Hershkowitz DAVID EMAIL Michael L. Littman MICHAEL EMAIL Brown University, 115 Waterman Street, Providence, RI 02906 |
| Pseudocode | No | The paper contains mathematical derivations and proofs but no structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code base1 provides implementations for abstracting arbitrary MDPs as well as visualizing and evaluating the resulting abstract MDPs. 1github.com/david-abel/state_abstraction |
| Open Datasets | No | The paper describes environments like 'NChain', 'Taxi', 'Minefield', and 'Random MDP' which are used in experiments, but it does not provide concrete access information (specific link, DOI, repository name, or formal citation with authors/year) for publicly available datasets as 'training data' in the typical sense. |
| Dataset Splits | No | The paper mentions running trials and confidence intervals for results but does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like Graph Stream and BURLAP but does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For all experiments, we set γ to 0.95. ... In the Taxi and Random domains, 200 trials were run for each data point, whereas 20 trials were sufficient in Minefield and NChain. |