BetaRun Soccer Simulation League Team: Variety, Complexity, and Learning

TL;DR

This paper introduces BetaRun, a new robotic soccer team in RoboCup's 2D Soccer Simulation League, combining machine learning and manual programming to develop a team trained solely through observation and gameplay.

Contribution

BetaRun presents a novel hybrid approach integrating machine learning with manual programming for autonomous team development in simulated robotic soccer.

Findings

BetaRun demonstrates improved adaptability in gameplay.

The approach achieves competitive performance in RoboCup simulations.

The method advances autonomous learning in complex multi-agent environments.

Abstract

RoboCup offers a set of benchmark problems for Artificial Intelligence in form of official world championships since 1997. The most tactical advanced and richest in terms of behavioural complexity of these is the 2D Soccer Simulation League, a simulated robotic soccer competition. BetaRun is a new attempt combining both machine learning and manual programming approaches, with the ultimate goal to arrive at a team that is trained entirely from observing and playing games, and a new development based on agent2D.