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..
State-Based Recurrent SPMNs for Decision-Theoretic Planning under Partial Observability
Authors: Layton Hayes, Prashant Doshi, Swaraj Pawar, Hari Teja Tatavarti
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test the performance of the learning algorithm by learning S-RSPMNs on a testbed of several sequential decision-making domains from Open AI s Gym [Brockman et al., 2016] and RDDLSim [Sanner, 2010], demonstrating that they result in nearly optimal policy values for each. |
| Researcher Affiliation | Academia | 1 Institute for AI, University of Georgia, Athens GA 30602 2 Dept. of Computer Science, University of Georgia, Athens, GA 30602 EMAIL |
| Pseudocode | Yes | Algorithm 1 gives the main procedure, LEARNS-RSPMN, for learning the S-RSPMN template. |
| Open Source Code | Yes | The LEARNS-RSPMN algorithm has been implemented in the SPFlow library [Molina et al., 2019] and is available on Git Hub at https://github.com/minimum-Layton C/SPFlow/tree/ rspmn rdc rmeuο¬x under the Apache license. |
| Open Datasets | Yes | As there are very few existing data sets on simulations of discrete partially observable decision-making domains, we developed a new testbed of eight data sets on decision-making problems, listed in Table 1 and available at https://github.com/ minimum-Layton C/SRSPMN dataset generators. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits or information on cross-validation. |
| Hardware Specification | Yes | All models were learned on a PC with Intel Xeon ES-2603, RHEL7, 16GB RAM. |
| Software Dependencies | No | The paper mentions the "SPFlow library" but does not provide a specific version number. It also mentions "RHEL7" which is an operating system, not an ancillary software dependency with a version. |
| Experiment Setup | Yes | Learning an S-RSPMN requires setting two parameters: horizon h, correlation threshold cthresh. Both S-RSPMN and BCQ models for all domains except Navigation were run for 100 steps (to obtain near-converged values) whereas the Navigation models were evaluated over 10 steps. All other BCQ parameters such as the number of samples and loopback values were set to default. |