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..
Safe Reinforcement Learning via Curriculum Induction
Authors: Matteo Turchetta, Andrey Kolobov, Shital Shah, Andreas Krause, Alekh Agarwal
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments use this framework in two challenging environments to induce curricula for safe and ef๏ฌcient learning. |
| Researcher Affiliation | Collaboration | Matteo Turchetta Department of Computer Science ETH Zurich EMAIL Andrey Kolobov Microsoft Research Redmond, WA-998052 EMAIL Shital Shah Microsoft Research Redmond, WA-998052 EMAIL Andreas Krause Department of Computer Science ETH Zurich EMAIL Alekh Agarwal Microsoft Research Redmond, WA-998052 EMAIL |
| Pseudocode | Yes | Algorithm 1 CISR |
| Open Source Code | Yes | We release an open source implementation of CISR and of our experiments2. 2https://github.com/zuzuba/CISR_NeurIPS20 |
| Open Datasets | Yes | Frozen Lake and the Lunar Lander environments from Open AI Gym [10]. |
| Dataset Splits | No | No. The paper describes the training process in RL environments (Frozen Lake and Lunar Lander) where data is generated through interaction, but it does not specify explicit training/validation/test dataset splits with percentages, sample counts, or citations to predefined splits. It mentions evaluating policies in the original environment but not how a static dataset would be split for validation. |
| Hardware Specification | No | No. The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | No. The paper mentions using 'Stable Baselines [25] implementation of PPO [43]' and 'GP-UCB [44]' for optimization, but it does not provide specific version numbers for these software components or other dependencies. |
| Experiment Setup | Yes | For a detailed overview of the hyperparameters and the environments, see Appendices A and B. |