Retaining Learned Behavior During Real-Time Neuroevolution

  title={Retaining Learned Behavior During Real-Time Neuroevolution},
  author={Thomas D'Silva and Roy Janik and Micahel Chrien and Kenneth O. Stanley and Risto Miikkulainen},
Creating software-controlled agents in videogames who can learn and adapt to player behavior is a difficult task. Using the real-time NeuroEvolution of Augmenting Topologies (rtNEAT) method for evolving increasingly complex artificial neural networks in real-time has been shown to be an effective way of achieving behaviors beyond simple scripted character behavior. In NERO, a videogame built to showcase the features of rtNEAT, agents are trained in various tasks, including shooting enemies… CONTINUE READING
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