Evolving Keepaway Soccer Players through Task Decomposition

@inproceedings{Whiteson2003EvolvingKS,
  title={Evolving Keepaway Soccer Players through Task Decomposition},
  author={Shimon Whiteson and Nate Kohl and Risto Miikkulainen and Peter Stone},
  booktitle={GECCO},
  year={2003}
}
In some complex control tasks, learning a direct mapping from an agent’s sensors to its actuators is very difficult. For such tasks, decomposing the problem into more manageable components can make learning feasible. In this paper, we provide a task decomposition, in the form of a decision tree, for one such task. We investigate two different methods of learning the resulting subtasks. The first approach, layered learning, trains each component sequentially in its own training environment… CONTINUE READING
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