Interactive Visualization for Debugging RL

TL;DR

This paper introduces an interactive visualization tool tailored for debugging and interpreting reinforcement learning algorithms, addressing unique challenges not covered by existing supervised learning visualization tools.

Contribution

The work presents a novel interactive visualization system specifically designed for RL debugging, incorporating features like different state representations and a flexible framework.

Findings

Enhanced understanding of RL policies through visualization

Facilitated debugging process for RL algorithms

Framework adaptable for future extensions

Abstract

Visualization tools for supervised learning allow users to interpret, introspect, and gain an 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 as these tools address challenges typically found in the supervised learning regime. In this work, we design and implement an interactive visualization tool for debugging and interpreting RL algorithms. Our system addresses many features missing from previous tools such as (1) tools for supervised learning often are not interactive; (2) while debugging RL policies researchers use state representations that are different from those seen by the agent; (3) a framework designed to make the debugging RL policies more conducive. We provide an example workflow of how this system could be used, along with ideas for future extensions.