
## Training and Evaluation of Deep Policies Using Reinforcement Learning and Generative Models
Ali Ghadirzadeh, Petra Poklukar, Karol Arndt, Chelsea Finn, Ville Kyrki, Danica Kragic, Mårten Björkman
Keywords: 
JMLR/2022/Proceedings/201265 - Training and Evaluation of Deep Policies Using Reinforcement Learning and Generative Models.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?_

[10]

No implementation provided. Pseudo code of their method in algorithm 1. 

### 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]

(2/2)

The authors use two simulation environments, describe them and illustrate it in figure 2, and state the engine used for the simulation but do not provide any link regarding the implementation.

### 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?_

[3]

The authors vary the hyperparmaeters Na and B in table 1 and show the results (Grid). Not all HPs are experimented with however. 

### 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 report average reward with std dev acroos three random seeds. 

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

[4]

Requires expertise on RL.
