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Applied Research
Emulating real soccer
Abstract
We use simulated soccer to study multi-agent learning. Each team member tries to learn from the corresponding human player in a real game. Following a unified approach, strategic and tactical behavior is learned synergistically by training a feed-forward neural network (ANN) with a modified back-propagation algorithm. It aims at decreasing the learning time and avoiding the local maximums. We tried to minimize the computation effort, as required in classic back-propagation (BKP) methods.
