
## Probabilistic Offline Policy Ranking with Approximate Bayesian Computation
Longchao Da, Porter Jenkins, Trevor Schwantes, Jeffrey Dotson, Hua Wei
Keywords: RU: Probabilistic Inference, ML: Bayesian Learning, ML: Reinforcement Learning, RU: Uncertainty Representations
AAAI/2024/Proceedings/31792 - Probabilistic Offline Policy Ranking with Approximate Bayesian Computation.pdf

### 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?_

[7]

The authors provide a github link (https://github.com/LongchaoDa/POPR-EABC.git) to their implementation, and also the baseline used. Their code repository however has an empty readme, and the code seems to be incomplete and in general lacks documentation. This makes it difficult to use, if at all helpful. No installation notes, examples or other information.

### 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?_

[2]

(4/4)

The authors use four publicly available environments. However the authors do not provide descriptions on them. 

### 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?_

[7]

The authors specify some hyperparameter values of the method, but a full description seems to be lacking. It can not be found in their repository either. Acquisition method/budget unclear.

### 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 describe how they evaluated each model against each environment and which metrics were collected. 

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

[7]

To reproduce the method requires a deep understanding of reinforcement learning methods.
