Provably Efficient Model-Free Constrained RL with Linear Function Approximation
Authors: Arnob Ghosh, Xingyu Zhou, Ness Shroff
NeurIPS 2022 | Venue 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 EMAIL Xingyu Zhou Electrical and Computer Engineering Wayne State University Detroit, MI, USA EMAIL Ness Shroff Electrical and Computer Engineering The Ohio State University Columbus, OH, USA EMAIL |
| 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. |