Year: 2024
Type: article
Abstract: 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.
Source: AI Magazine
DOI: 10.1002/aaai.12190
Author: Josiah P. Hanna
Institution: University of Wisconsin–Madison
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,400
OpenAlex ID: W4402724002