We propose a reinforcement learning solution to the soccer dribbling task, a scenario in which a soccer agent has to go from the beginning to the end of a region keeping possession of the ball, as an adversary attempts to gain possession. While the adversary uses a stationary policy, the dribbler learns the best action to take at each decision point. After defining meaningful variables to represent the state space, and high-level macro-actions to incorporate domain knowledge, we describe our application of the reinforcement learning algorithm Sarsa with CMAC for function approximation. Our experiments show that, after the training period, the dribbler is able to accomplish its task against a strong adversary around 58 of the time.
@cite_11: Keepaway is a sub-problem of RoboCup Soccer Simulator in which 'the keepers' try to maintain the possession of the ball, while 'the takers' try to steal the ball or force it out of bounds. By using Reinforcement Learning as a learning method, a lot of research has been done in this domain. In these works, there has been a remarkable success for the intelligent keepers part, however most of these keepers are trained and tested against simple hand-coded takers. We tried to address this part of the problem by using Sarsa(λ) as a Reinforcement Learning method with linear tile-coding as function approximation and used two different state spaces that we specially designed for the takers. As the results of the experiments confirm, we outperformed the hand-coded taker which results in creating a better trainer and tester for the keepers. Also when designing the new state space, we noticed that smaller state spaces can also be successful for this part of the problem.
@cite_8: RoboCup simulated soccer presents many challenges to reinforcement learning methods, including a large state space, hidden and uncertain state, multiple independent agents learning simultaneously, and long and variable delays in the effects of actions. We describe our application of episodic SMDP Sarsa(λ) with linear tile-coding function approximation and variable λ to learning higher-level decisions in a keepaway subtask of RoboCup soccer. In keepaway, one team, “the keepers,” tries to keep control of the ball for as long as possible despite the efforts of “the takers.” The keepers learn individually when to hold the ball and when to pass to a teammate. Our agents learned policies that significantly outperform a range of benchmark policies. We demonstrate the generality of our approach by applying it to a number of task variations including different field sizes and different numbers of players on each team.
Arguably, the most successful application is due to Stone @cite_8 . They propose the keepaway task", which consists of two teams, the keepers and the takers, where the former tries to keep control of the ball for as long as possible, while the latter tries to gain possession. Our solution to the soccer dribbling task follows closely the solution proposed by those authors to learn the keepers' behavior. Iscen and Erogul @cite_11 use similar solution to learn a policy for the takers.