
## Anytime-Competitive Reinforcement Learning with Policy Prior
Jianyi Yang, Pengfei Li, Tongxin Li, Adam Wierman, Shaolei Ren
Keywords: 
NeurIPS/2023/Proceedings/72273 - Anytime-Competitive Reinforcement Learning with Policy Prior.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 is not given.

### 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]

(1/1)

The authors experiment with carbo-aware computing application and provide a citation on it. The problem is formulated in appendix A.1. Parameters are stated in A.3. The implementation of the simulation is not given, but the dataset it is based on is cited.

### 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?_

[4]

Hyperparameters are mentioned in appendix A.3. Structural overview not given and acquisition not specified.

### 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 measure regret per epsiode (RL metric). The results are training on the simulator (no split applicable). 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?_

[7]

Requries expertise (including a solid theory expertise) on MDP.
