Real-time Learning in the NERO Video Game

Kenneth O. Stanley, Ryan Cornelius, and Risto Miikkulainen

If game characters could learn through interacting with the player, behavior could improve as the game is played, keeping it interesting. The real-time NeuroEvolution of Augmenting Topologies (rtNEAT) method, which can evolve increasingly complex artificial neural networks in real time as a game is being played, will be presented. The rtNEAT method makes possible an entirely new genre of video games in which the player trains a team of agents through a series of customized exercises. In order to demonstrate this concept, the NeuroEvolving Robotic Operatives (NERO) game was built based on rtNEAT. In NERO, the player trains a team of virtual robots for combat against other players’ teams. The live demo will show how agents in NERO adapt in real time as they interact with the player. In the future, rtNEAT may allow new kinds of educational and training applications through interactive and adapting games.

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