Abstract
This paper describes a reinforcement learning-based strategy developed for Robocup simulator league competition. In overview: each agent is provided a common set of skills (motor schema-based behavioral assemblages) from which it builds a task-achieving strategy using reinforcement learning. The agents learn individually to activate particular behavioral assemblages given their current situation and a reward signal. The entire team is jointly rewarded or penalized when they score or are scored against (global reinforcement). This approach provides for diversity in individual behavior.
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© 1998 Springer-Verlag Berlin Heidelberg
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Balch, T. (1998). Integrating learning with motor schema-based control for a Robot Soccer Team. In: Kitano, H. (eds) RoboCup-97: Robot Soccer World Cup I. RoboCup 1997. Lecture Notes in Computer Science, vol 1395. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64473-3_86
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DOI: https://doi.org/10.1007/3-540-64473-3_86
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