
## Do Differentiable Simulators Give Better Policy Gradients?
Hyung Ju Suh, Max Simchowitz, Kaiqing Zhang, Russ Tedrake
Keywords: 
Award: Outstanding Paper
ICML/2022/Proceedings/743 - Do Differentiable Simulators Give Better Policy Gradients?.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]

Implementation not provided. The authors state they use Torch and provide a citation on it as framework. 

### 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?_

[8]

(0/3)

The authors use simulated environments for their experiments and give brief descriptions but no implementations. 

### 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 has a parameter alpha, which is varied in the experiments.

### 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?_

[5]

The authors present the results with standard deviation over 10 folds in some plots but its not stated everywhere. The metrics are not always clear. 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?_

[7]

Requires expertise on policy graadients.
