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..
Constraint-Adaptive Policy Switching for Offline Safe Reinforcement Learning
Authors: Yassine Chemingui, Aryan Deshwal, Honghao Wei, Alan Fern, Jana Doppa
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on 38 tasks from the DSRL benchmark demonstrate that CAPS consistently outperforms existing methods, establishing a strong wrapper-based baseline for OSRL. |
| Researcher Affiliation | Academia | Yassine Chemingui1, Aryan Deshwal2, Honghao Wei1, Alan Fern3, Jana Doppa1 1Washington State University 2University of Minnesota 3Oregon State University EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the algorithms and methods in prose and mathematical equations in Sections 4.1 and 4.2, but it does not include a distinct pseudocode block or algorithm figure. |
| Open Source Code | Yes | The code/appendices are available at https://github.com/ yassine Ch/CAPS. |
| Open Datasets | Yes | We employ 38 sequential decision-making benchmarks of varying difficulty from Safety-Gymnasium (Ray, Achiam, and Amodei 2019; Ji et al. 2024), Bullet Safety-Gym (Gronauer 2022), and Meta Drive (Li et al. 2022) within the DSRL framework (Liu et al. 2024). Further details are provided in Appendix C.1. |
| Dataset Splits | No | The paper mentions using a "fixed pre-collected dataset D" and evaluating algorithms with "three random seeds, and twenty episodes," but it does not specify explicit training, validation, or test splits for this dataset. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using existing offline RL methods like IQL and SAC+BC but does not specify software libraries, frameworks, or their version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We provide the details of the neural network structure used for value and Q-functions, policy heads, and hyper-parameters in the Appendix C.3. |