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..
Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms
Authors: Pinar Ozisik, Philip S. Thomas
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We introduce a new algorithm that is more robust against data corruptions, and demonstrate its usage in practice on some RL problems, including a grid-world and a diabetes treatment simulation. ... Finally, we present experiments that support our theoretical analysis. |
| Researcher Affiliation | Academia | A. Pinar Ozisik1 Philip S. Thomas1 1College of Information and Computer Sciences University of Massachusetts EMAIL |
| Pseudocode | Yes | For pseudocode, refer to Algorithm 1 in Appendix C. |
| Open Source Code | No | The paper refers to a third-party simulator's code ('Simglucose v0.2.1 (2018), 2019. URL https://github.com/jxx123/simglucose.'), but it does not provide explicit statements or links for the authors' own source code for the methodology or algorithm described in the paper. |
| Open Datasets | Yes | For the diabetes treatment simulation, we use a Python implementation [47] of an FDA-approved type 1 diabetes Mellitus simulator (T1DMS) by Kovatchev et al. [20] and Man et al. [28]. |
| Dataset Splits | No | The paper mentions creating a dataset of a specific size ('to a randomly created D of size 1,500') but does not provide explicit details on train, validation, or test dataset splits (e.g., percentages or sample counts) for reproducibility of data partitioning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | Yes | For the diabetes treatment simulation, we use a Python implementation [47] of an FDA-approved type 1 diabetes Mellitus simulator (T1DMS) by Kovatchev et al. [20] and Man et al. [28]. [47] Jinyu Xie. Simglucose v0.2.1 (2018), 2019. URL https://github.com/jxx123/simglucose. |
| Experiment Setup | Yes | For our experimental setup, we selected two policies per domain. We estimated J(πb) 0.797 and J(πe) 0.728 for grid-world, and J(πb) 0.218 and J(πe) 0.145 for the diabetes domain, by averaging returns obtained from running each policy 10,000 times. We added k adversarial trajectories based on the optimal attacker strategy to a randomly created D of size 1,500. ... Figure 1 shows the average LCH, over 750 trials, as k increases. |