Vizarel: A System to Help Better Understand RL Agents

Visualization tools for supervised learning have Visualization systems at their core consist of two components: allowed users to interpret, introspect, and gain representation and interaction. Though these may intuition for the successes and failures of their appear to be disparate, it is hard to discount the influence models. While reinforcement learning practitioners that each has on each other. The tools we use for representation ask many of the same questions, existing tools affect how we interact with the system, and our are not applicable to the RL setting. In this work, interaction affects the representations that we create (Yi we describe our initial attempt at constructing et al., 2007). Visualization interfaces should adhere to the a prototype of these ideas, through identifying human action cycle (Norman, 2013), which provides us possible features that such a system should encapsulate.