
## Misspecification in Inverse Reinforcement Learning
Joar Skalse, Alessandro Abate
Keywords: General
Award: Outstanding Paper Award
AAAI/2023/Proceedings/26766 - Misspecification in Inverse Reinforcement Learning.pdf

[X] Theoretical

### 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?_

[1]

The authors provide a mathematical framework for analysing misspecification. Implementation is not relevant.

### 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?_

[1]

(0/0)

No empirical results are produced in this paper.

### 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]

No hyperparameter/algorithm configuration values are applicable in this paper.

### 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]

No empirical results are produced in this paper.

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

[10]

The mathematical framework presented here requires a deep understanding of the fundamental mathematics of statistical modelling, specifically regarding (inverse) reinforcement learning. 
