Machine learning in simulated soccer

Andrade, Anthony John; Zenchenko, Timothy Iosifovich; Milette, Gregory Peter

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Machine learning in simulated soccer

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Our project used decision tree learning in combination with layered learning to teach agents soccer skills within the Robocup simulated soccer environment. We examined the numerous factors affecting the agent's ability to learn. We successfully trained the agent to perform two lower level skills, passing and shooting. Then we had similar success training the agent to use an upper level skill that decides between the two lower level skills. We also identified parameters that allowed for maximum learning.

This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

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