Toward the confident deployment of real-world reinforcement learning agents

Intelligent learning agents must be able to learn from experience so as to accomplish tasks that require more ability than could be initially programmed. Reinforcement learning (RL) has emerged as a potentially powerful class of solution methods to create agents that learn from trial-and-error interaction with the world. Despite many prominent success stories, a number of challenges often stand between the use of RL in real-world problems. As part of the AAAI New Faculty Highlight Program, in this article, I will describe the work that my group is doing at the University of Wisconsin-Madison with the intent to remove barriers to the use of RL in practice. Specifically, I will describe recent work that aims to give practitioners confidence in learned behaviors, methods to increase the data efficiency of RL, and work on "challenge" domains that stress RL algorithms beyond current testbeds.