Provably Efficient Model-Free Constrained RL with Linear Function Approximation
Authors: Arnob Ghosh, Xingyu Zhou, Ness Shroff
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Algorithm 1 on a simulated model for job scheduling to validate our theoretical results. |
| Researcher Affiliation | Academia | Arnob Ghosh Electrical and Computer Engineering The Ohio State University Columbus, OH, USA ghosh.244@osu.edu Xingyu Zhou Electrical and Computer Engineering Wayne State University Detroit, MI, USA xingyu.zhou@wayne.edu Ness Shroff Electrical and Computer Engineering The Ohio State University Columbus, OH, USA shroff.11@osu.edu |
| Pseudocode | Yes | Algorithm 1 Model Free Primal-Dual Algorithm for Linear Function Approximation |
| Open Source Code | No | The paper states in a checklist that code is included, but does not provide a URL or explicit mention within the main body of the text for public access. |
| Open Datasets | No | The paper uses a 'simulated model for job scheduling' and does not provide concrete access information or citations for a publicly available dataset. |
| Dataset Splits | No | The paper uses a simulated model for experiments and does not specify any train/validation/test dataset splits or their methodology. |
| Hardware Specification | No | The paper does not provide any specific hardware details for running the experiments, and the authors state 'N/A' for compute resources in the checklist. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | The parameters we used are the followings: α = K/(1 + 2H/γ + H), η = 2H/(γ KH2). We set γ = 1. We have also set ϵ = 0.1 in order to ensure that the violation goes towards 0 as K increases. |