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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Regular Decision Processes: A Model for Non-Markovian Domains
Authors: Ronen I. Brafman, Giuseppe De Giacomo
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce and study Regular Decision Processes (RDPs), a new, compact, factored model for domains with non-Markovian dynamics and rewards. ... The main contributions of this paper are to introduce RDPs, study their properties and complexity, describe how to optimize them, and postulate a potential method for learning them from observations. |
| Researcher Affiliation | Academia | 1Ben-Gurion University, Israel 2Sapienza Universit a di Roma, Italy EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Pseudocode for Learning Finite NMDPs |
| Open Source Code | No | The paper mentions automated tools for constructing DFAs (e.g., https://ο¬loat.herokuapp.com [Favorito, 2018] http://ltlf2dfa.diag.uniroma1.it [Fuggitti, 2018]) but does not provide a link or explicit statement about the availability of source code for the methodology described in this paper. |
| Open Datasets | No | The paper is theoretical and does not mention using any dataset for training. No information about publicly available datasets or their access is provided. |
| Dataset Splits | No | The paper is theoretical and does not mention any dataset splits (training, validation, or test). Therefore, no specific dataset split information is provided. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running experiments, as it is a theoretical work. |
| Software Dependencies | No | The paper mentions external tools (e.g., float.herokuapp.com, ltlf2dfa.diag.uniroma1.it) but does not provide specific version numbers for these or any other ancillary software dependencies used in their own work. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details, hyperparameters, or training configurations. |