Batch Reinforcement Learning on a RoboCup SSL Keepaway Strategy Learning Problem

Abstract

Robotic soccer provides an adversarial scenario where collaborative agents have to execute actions by following a hand-coded or a learned strategy, which in the case of the Small Size League, is given by a centralized decision maker.This work takes advantage of this centralized approach for modelling the keepaway strategy learning problem which is inherently multi-agent, as a single-agent problem, where now each robot forms part of the state of the model.One of the classical reinforcement learning methods is compared with its batch version in terms of amount of time for learning and concluding about updates efficiency based on experiences reusability.