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 Policy Optimization with Local Generalized Linear Function Approximations
Authors: Akifumi Wachi, Yunyue Wei, Yanan Sui
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of our SPO-LF in two experiments. |
| Researcher Affiliation | Collaboration | Akifumi Wachi IBM Research EMAIL Yunyue Wei Tsinghua University EMAIL Yanan Sui Tsinghua University EMAIL |
| Pseudocode | Yes | Algorithm 1 SPO-LF with ETSE |
| Open Source Code | Yes | For future research, our code is open-sourced.3 https://github.com/akifumi-wachi-4/spolf |
| Open Datasets | Yes | We constructed a simulation environment based on Gym-Mini Grid [12]. |
| Dataset Splits | No | The paper describes providing initial samples and discretizing the environment, but it does not specify explicit train, validation, or test dataset splits (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | No | The paper discusses computational cost and efficiency but does not provide specific details regarding the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions environments like Gym-Mini Grid and Safety-Gym, but it does not list specific software dependencies with their version numbers (e.g., Python, PyTorch, TensorFlow versions) that are crucial for replication. |
| Experiment Setup | Yes | Settings. We considered a 25 25 grid in which each grid was associated with a randomly generated feature vector with the dimension d = 5... Finally, we set γ = 0.999, δr = δg = 0.05, and h = 0.1, and optimized a policy with policy iteration. |