BetaRun Soccer Simulation League Team: Variety, Complexity, and Learning
This work addresses the problem of developing autonomous agents for complex, multi-agent environments like robotic soccer, which is incremental as it builds on prior methods.
The paper tackles the challenge of creating a competitive robotic soccer team in the 2D Soccer Simulation League by combining machine learning and manual programming, with the goal of achieving full training from game observation and play, building on the existing agent2D framework.
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.