Using Action-Policy Testing in RL to Reduce the Number of Bugs
Reinforcement learning is becoming ever more prominent in solving combinatorial search problems, in particular ones where states are images. Prior work has devised action-policy testing methodology, that identifies so-called bug states where policy performance is sub-optimal. Here we show how to leverage this methodology during the RL process, using action-policy testing to find bugs and injecting those as alternate start states for the training runs. Running experiments across six 2D games, we find that our testing-guided training often achieves similar expected reward while reducing the number of bugs.
Authors
- Hasan Ferit Enişer (ORCID: https://orcid.org/0000-0002-3259-8794)
- Songtuan Lin
- Maria Christakis (ORCID: https://orcid.org/0000-0002-2649-1958)
- Jörg Hoffmann (ORCID: https://orcid.org/0000-0003-1590-5876)
- Valentin Wüstholz (ORCID: https://orcid.org/0000-0003-1496-1104)
- N. Müller (ORCID: https://orcid.org/0000-0002-5932-3395)
- Anastasia Isychev (ORCID: https://orcid.org/0000-0001-6375-0421)
- Isabel Valera
Institutions
- TU Wien (AT)
- German Research Centre for Artificial Intelligence (DE)
- ConsenSys (United States) (US)
- Max Planck Institute for Software Systems (DE)
- Saarland University (DE)
Publication Details
- Journal
- Proceedings of the International Symposium on Combinatorial Search
- Published
- 2025-07-19
- DOI
- https://doi.org/10.1609/socs.v18i1.35990
- Primary Topic
- Insect-Plant Interactions and Control
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Deutsche Forschungsgemeinschaft
- Bundesministerium für Bildung und Forschung