Reinforcement Learning for the Soccer Dribbling Task

Arthur Carvalho; Renato Oliveira

doi:10.1109/cig.2011.6031994

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arXiv · 1305.6568

Reinforcement Learning for the Soccer Dribbling Task

Arthur Carvalho·Renato Oliveira

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Abstract

We propose a reinforcement learning solution to the \emph{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 \emph{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.

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Arthur Carvalho, Renato Oliveira. 2013-05-28. Reinforcement Learning for the Soccer Dribbling Task. https://doi.org/10.1109/cig.2011.6031994

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