Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function
Authors: Aviv Rosenberg, Yishay Mansour
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The algorithms are fairly simple, and the main challenge is the analysis of the regret and computational complexity. |
| Researcher Affiliation | Collaboration | Aviv Rosenberg Tel Aviv University, Israel avivros007@gmail.com Yishay Mansour Tel Aviv University, Israel and Google Research, Israel mansour.yishay@gmail.com |
| Pseudocode | Yes | The efficient implementation of this algorithm is similar to the one of the original UC-O-REPS algorithm, and is described in details in the supplementary material (together with full pseudo-code). |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It mentions pseudocode in supplementary material, but not executable source code. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments involving datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, therefore no dataset splits are described. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details, hyperparameters, or training configurations. |