
## On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control
Amrit Singh Bedi, Anjaly Parayil, Junyu Zhang, Mengdi Wang, Alec Koppel
Keywords: 
JMLR/2024/Proceedings/211343 - On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control.pdf
Project URL: nan

### 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?_

[9]

No implementation given, but the authors do state the library they use in the appendix for simulations.

### 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]

(2/2)

The authors use Pathological Mountain Car and 1D Mario environment and provide citations on them. The environments are described and parameters given. No implementation given/links.

### 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]

The authors state the parameters of the method in algorithm 1. Their values are discussed in section 6 where some are directly given and some are specified as ranges. No acquisition specified. No strucutral overview.

### 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 average cumulative return (environment). The repitition and average are not clear.

### 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 on policy search.
