
## Robust phi-Divergence MDPs
Chin Pang Ho, Marek Petrik, Wolfram Wiesemann
Keywords: 
NeurIPS/2022/Proceedings/54736 - Robust phi-Divergence MDPs.pdf
Project URL: https://sites.google.com/view/clint-chin-pang-ho

### 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?_

[2]

The authors provide a link to a project page where a download link for the implementation can be found (https://drive.google.com/uc?export=download&id=18knRApQXbAftncyujU1FtdIJ2GFUT5KU). The code has no readme. The code has okay comments. No installation/compilation instructions.

### 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?_

[1]

(1/1)

The authors generate instances (synthetic) and describe the generation in section 6. The generator is included in the implementation. The generator does not take any parameters.

### 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?_

[1]

The method is parameter free.

### 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 generate 50 random instances and report averages. They measure runtime is ms. Data split not applicable. 

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

[4]

Requries expertise on MDPs.
