  Progress in fields of machine learning and adversarial planning has benefited
significantly from benchmark domains, from checkers and the classic UCI data
sets to Go and Diplomacy. In sequential decision-making, agent evaluation has
largely been restricted to few interactions against experts, with the aim to
reach some desired level of performance (e.g. beating a human professional
player). We propose a benchmark for multiagent learning based on repeated play
of the simple game Rock, Paper, Scissors along with a population of forty-three
tournament entries, some of which are intentionally sub-optimal. We describe
metrics to measure the quality of agents based both on average returns and
exploitability. We then show that several RL, online learning, and language
model approaches can learn good counter-strategies and generalize well, but
ultimately lose to the top-performing bots, creating an opportunity for
research in multiagent learning.

Computer Science and Game Theory;Artificial Intelligence;Machine Learning;Multiagent Systems