Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Learning and Solving Regular Decision Processes
Authors: Eden Abadi, Ronen I. Brafman
IJCAI 2020 | Venue PDF | LLM Run Details | Input Tokens: 15,507 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,472 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate this approach, demonstrating its feasibility. |
| Researcher Affiliation | Academia | Eden Abadi and Ronen I. Brafman Department of Computer Science, Ben Gurion University, Israel EMAIL, EMAIL |
| Pseudocode | Yes | The high-level pseudo-code of our algorithm, S3M (Sample, Merge, Mealy Machine), is given in Algorithm 1. |
| Open Source Code | No | The paper does not provide an explicit statement or link to its source code. |
| Open Datasets | No | The paper introduces "two new RDP domains: NM-MAB and Rotating Maze" which are custom domains and does not provide concrete access information (link, DOI, formal citation) for publicly available datasets. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts) for training, validation, or test sets, nor does it refer to predefined splits with citations or detailed splitting methodology. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using "EDSM implementation from the Flex Fringe library" and "UCT" but does not provide specific version numbers for these software components. |
| Experiment Setup | No | The paper mentions parameters like 'c' for UCB1, 'α' and 'ϵ' for Q-learning, 'min samples', 'ϵ' for KL divergence merging, and 'λ' for the loss function, but does not provide their specific numerical values. |