
## Low-Rank Representation of Reinforcement Learning Policies
Bogdan Mazoure, Thang Doan, Tianyu Li, Vladimir Makarenkov, Joelle Pineau, Doina Precup, Guillaume Rabusseau
Keywords: 
JAIR/2022/Proceedings/13854 - Low-Rank Representation of Reinforcement Learning Policies.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?_

[6]

The authors state the code will be available on GitHub upon publication, but no link is provided. They also state the code is included with supplemental files as a zip, but JAIR does not provide this online. We decided to make an exception in this case as mistakes could have been made with the publication process itself, and check GitHub.com for the title of the repository, but find nothing. A second attempt with Google, with search '$TITLE$ "GitHub"' also yields no results. The authors do provide detailed Python pseudo code in Appendix A.9.1. which also provides information on which packages are used. This makes re-implementation very accesible, but will still require some work.

### 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]

(3/3)

The authors use simulated environments and provide a citation on the lbirary from which they use these. It includes Continuous Mountain Car, bandit turntable and Pendulum. They are described in appendix A.9.

### 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?_

[2]

The authors state HPs in table 2 of appendix A.9.3. per environment. The authors state on which objective they determined the configurations (empirically) but not under which budget.

### 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?_

[3]

The authors evaluate the method with average wasserstein-1 metric and provide a formula in 5.1. They also use variance of the returns (environment) and number of parameters (model). Population is not always clear, variation and aggregation not specified.

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

[5]

Requires expertise on RL policies.
