Code, benchmarks, experiment data, and additional results for the ICAPS 2026 paper "Operator-Counting Heuristics for Domain-Independent Dynamic Programming"
Code, benchmarks, experiment data, and additional results for the ICAPS 2026 paper "Operator-Counting Heuristics for Domain-Independent Dynamic Programming" It contains: `supplementary.pdf`: a PDF containing detailed results explaining the findings, technical details of the DIDP definition and the translation to SMT `didp-rs-abstract-interpretation.zip`: source code (see README for build instructions) `benchmarks.zip`: DIDP models for the four problem classses: BPP, m-PDTSP, TSP, Talent Scheduling with instance `experiment-data.zip`: experiment run logs and scripts
Authors
- Malte Helmert (ORCID: https://orcid.org/0009-0008-8462-350X)
- Florian Pommerening (ORCID: https://orcid.org/0000-0003-1311-283X)
- Tanja Schindler
- Chris Beck
Institutions
- University of Basel (CH)
- University of Toronto (CA)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-04-13
- DOI
- https://doi.org/10.5281/zenodo.19560995
- Primary Topic
- Reinforcement Learning in Robotics
- Type
- article
- Field-Weighted Citation Impact
- 0.00