Towers of Saliency: A Reinforcement Learning Visualization Using Immersive Environments

Nathan Douglas, Dianna Yim, Bilal Kartal, Pablo Hernandez-Leal, Frank Maurer, Matthew E. Taylor

Abstract: Deep reinforcement learning (DRL) has had many successes on complex tasks, but is typically considered a black box. Opening this black box would enable better understanding and trust of the model which can be helpful for researchers and end users to better interact with the learner. In this paper, we propose a new visualization to better analyze DRL agents and present a case study using the Pommerman benchmark domain. This visualization combines two previously proven methods for improving human understanding of systems: saliency mapping and immersive visualization.