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..
Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems
Authors: Rushang Karia, Rashmeet Kaur Nayyar, Siddharth Srivastava
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical analysis shows that this approach effectively learns broadly applicable policy knowledge in a few-shot fashion and signi cantly outperforms state-of-the-art SSP solvers on test problems whose object counts are far greater than those used during training. |
| Researcher Affiliation | Academia | Rushang Karia, Rashmeet Kaur Nayyar, Siddharth Srivastava School of Computing and Augmented Intelligence Arizona State University Tempe, AZ 85281 U.S.A. EMAIL |
| Pseudocode | Yes | Algorithm 1 GPA acceleration for SSPs |
| Open Source Code | Yes | Our source code is available at https://github.com/AAIR-lab/GRAPL |
| Open Datasets | Yes | We utilized problem generators from the IPC and IPPC suites and those in Shah et al. (2020) for generating the training and test problems for all domains. |
| Dataset Splits | No | The paper describes using a small set of training instances and a separate test set, but it does not mention a distinct validation set or specific train/validation/test splits with percentages or sample counts for a single dataset. |
| Hardware Specification | Yes | We ran our experiments on a cluster of Intel Xeon E5-2680 v4 CPUs running at 2.4 GHz with 16 Gi B of RAM. |
| Software Dependencies | No | The paper states, "Our implementation is a Python adaptation of mdp-lib," but does not specify version numbers for Python, mdp-lib, or any other software libraries or solvers used. |
| Experiment Setup | Yes | We xed the time and memory limit for each problem to 7200 seconds and 16 Gi B respectively. ... Additional information of our empirical setup such as problem parameters, hyperparameters used for con guring baselines, etc., is included in Appendix B. |