Visualization tools for supervised learning have allowed users to interpret, introspect, and gain intuition for the successes and failures of their models. While reinforcement learning practitioners ask many of the same questions, existing tools are not applicable to the RL setting. In this work, we describe our initial attempt at constructing a prototype of these ideas, through identifying possible features that such a system should encapsulate. Our design is motivated by envisioning the system to be a platform on which to experiment with interpretable reinforcement learning.
@article{arxiv.2007.05577,
title = {Vizarel: A System to Help Better Understand RL Agents},
author = {Shuby Deshpande and Jeff Schneider},
journal= {arXiv preprint arXiv:2007.05577},
year = {2020}
}
Comments
Accepted to ICML 2020 Workshop on Human Interpretability in Machine Learning (Spotlight)