Abstract
We study multiagent learning in a simulated soccer scenario. Players from
the same team share a common policy for mapping inputs to actions. They
get rewarded or punished collectively in case of goals. For varying team
sizes we compare the following learning algorithms: TD-Q learning with
linear neural networks (TD-Q-LIN),
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