## Federated Q-Learning: Linear Regret Speedup with Low Communication Cost
Zhong Zheng, Fengyu Gao, Lingzhou Xue, Jing Yang
Keywords: 
ICLR/2024/Proceedings/18180 - Federated Q-Learning: Linear Regret Speedup with Low Communication Cost.pdf
Project URL: https://openreview.net/attachment?id=fe6ANBxcKM&name=supplementary_material

[X] Theoretical

### Implementation
_Given the documentation shared by the authors on a new method, how much effort would it be to re-implement the method from scratch?_

[1]

The authors do numerical experiments in the appendix and provide a link to the implementation in the reproducibility statement (https://openreview.net/attachment?id=fe6ANBxcKM&name=supplementary_material). However, still n/a as the main paper does not contain experiments.

### Data
_Given the data description in the documentation, how much effort would it take to either: Find the same data set the authors used, or a similar data set and defend the comparability, or acquire one from scratch?_

[1]

(0/0)

n/a

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for obtaining the reported results, and compare them against their computation budget?_

[1]

n/a

### Experimental Procedure
_Given the setup of experiments reported in the work, how difficult is it to set up a new experiment with the same procedure, similar to those presented in the original work?_

[1]

n/a

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying solely on the available documentation?_

[6]

-
