I am reading Zeibart's Inverse RL paper, and it states -
The agent is assumed to be attempting to optimize some function that linearly maps the features of each state, $f_{sj} \in \mathbb{R}^k$, to a state reward value representing the agent’s utility for visiting that state."
Can someone please give me an example of state features? I would highly appreciate it if it is in the context of this GitHub repo, wherein the author coded the feature_matrix as a diagonal matrix of shape $N \times D$, where $N$ represents states and $D$ features.
