  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.
