Leveraging Human Knowledge in Tabular Reinforcement Learning: A Study of Human Subjects

Leveraging Human Knowledge in Tabular Reinforcement Learning: A Study of Human Subjects

Ariel Rosenfeld, Matthew E. Taylor, Sarit Kraus

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 3823-3830. https://doi.org/10.24963/ijcai.2017/534

Reinforcement Learning (RL) can be extremely effective in solving complex, real-world problems. However, injecting human knowledge into an RL agent may require extensive effort on the human designer's part. To date, human factors are generally not considered in the development and evaluation of possible approaches. In this paper, we propose and evaluate a novel method, based on human psychology literature, which we show to be both effective and efficient, for both expert and non-expert designers, in injecting human knowledge for speeding up tabular RL.
Keywords:
Multidisciplinary Topics and Applications: Human-Computer Interaction
Multidisciplinary Topics and Applications: Knowledge-based Software Engineering
Machine Learning: Reinforcement Learning