Chimps: An evolutionary reinforcement learning approach for soccer agents

Abstract

In non-deterministic and dynamic environments, such as the RoboCup simulation league, it is necessary to simplify the search space to manage action selection in real time. In this work, we present Chimps, a team for RoboCup simulation league that uses an accuracy-based evolutionary reinforcement learning mechanism, called XCS to achieve this simplification. XCS is a Genetic Classifier System, with generalization capacities; we use them for the evolution of individual behavior's rules. We modified an existing team, 11Monkeys, that used static rules for individual action selection, adding an XCS to learn in real time over the outcome of individual actions. We found that our extension enhanced the team's performance.