Regular Decision Processes: A Model for Non-Markovian Domains
Authors: Ronen I. Brafman, Giuseppe De Giacomo
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | 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 brafman@cs.bgu.ac.il, degiacomo@dis.uniroma1.it |
| Pseudocode | Yes | Algorithm 1 Pseudocode for Learning Finite NMDPs |
| Open Source Code | No | The paper mentions automated tools for constructing DFAs (e.g., https://flloat.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. |