Online Learning of Counter Categories and Ratings in PvP Games

TL;DR

This paper introduces an online algorithm that updates player ratings and counter relationships in real-time for PvP games, addressing the limitations of scalar ratings in modeling intransitive strategies like rock-paper-scissors.

Contribution

It extends Elo-based ratings with an online update mechanism for counter categories, enabling real-time learning of complex intransitive relationships.

Findings

Effective in zero-sum competitive games

Handles intransitivity without neural networks

Suitable for real-time matchmaking scenarios

Abstract

In competitive games, strength ratings like Elo are widely used to quantify player skill and support matchmaking by accounting for skill disparities better than simple win rate statistics. However, scalar ratings cannot handle complex intransitive relationships, such as counter strategies seen in Rock-Paper-Scissors. To address this, recent work introduced Neural Rating Table and Neural Counter Table, which combine scalar ratings with discrete counter categories to model intransitivity. While effective, these methods rely on neural network training and cannot perform real-time updates. In this paper, we propose an online update algorithm that extends Elo principles to incorporate real-time learning of counter categories. Our method dynamically adjusts both ratings and counter relationships after each match, preserving the explainability of scalar ratings while addressing intransitivity. Experiments on zero-sum competitive games demonstrate its practicality, particularly in scenarios without complex team compositions.