
## Offline Reinforcement Learning with Differential Privacy
Dan Qiao, Yu-Xiang Wang
Keywords: 
NeurIPS/2023/Proceedings/71294 - Offline Reinforcement Learning with Differential Privacy.pdf
Project URL: 

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[10]

Implementation not given. Pseudo code in aglorithm 1/2.

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

[6]

(1/1)

The authors use simulated data, which they state and cite work for and provide modification details in footnote 11. The full settings and formulas can be found in appendix F. Implementation not given.

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

[10]

Values not discussed even though the pseudo code states various input parameters.

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

[1]

The authors run the experiments 5 times and present the average performance. The metric is suboptimality and the formula is in the caption of fig 1. There is no data split.

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

[8]

Requries expertise on RL and privacy.
