Incorporating Rivalry in Reinforcement Learning for a Competitive Game
Abstract
Recent advances in reinforcement learning with social agents have allowed us to achieve human-level performance on some interaction tasks. However, most interactive scenarios do not have as end-goal performance alone; instead, the social impact of these agents when interacting with humans is as important and, in most cases, never explored properly. This preregistration study focuses on providing a novel learning mechanism based on a rivalry social impact. Our scenario explored different reinforcement learning-based agents playing a competitive card game against human players. Based on the concept of competitive rivalry, our analysis aims to investigate if we can change the assessment of these agents from a human perspective.
Cite
@article{arxiv.2011.01337,
title = {Incorporating Rivalry in Reinforcement Learning for a Competitive Game},
author = {Pablo Barros and Ana Tanevska and Ozge Yalcin and Alessandra Sciutti},
journal= {arXiv preprint arXiv:2011.01337},
year = {2020}
}
Comments
Accepted at the Pre-registration Workshop @ NeurIPS2020