Real-time Neuroevolution in the NERO Video Game (2005)
Please note: This is a version of the paper that appears in IEEE Transactions on Evolutionary Computation. For the journal-formatted version, please visit the IEEE site.

In most modern video games, character behavior is scripted; no matter how many times the player exploits a weakness, that weakness is never repaired. Yet, if game characters could learn through interacting with the player, behavior could improve as the game is played, keeping it interesting. This paper introduces the real-time Neuroevolution of Augmenting Topologies (rtNEAT) method for evolving increasingly complex artificial neural networks in real time, as a game is being played. The rtNEAT method allows agents to change and improve during the game. In fact, rtNEAT makes possible an entirely new genre of video games in which the player trains a team of agents through a series of customized exercises. 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. This paper describes results from this novel application of machine learning, and demonstrates that rtNEAT makes possible video games like NERO where agents evolve and adapt in real time. In the future, rtNEAT may allow new kinds of educational and training applications through interactive and adapting games.
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IEEE Transactions on Evolutionary Computation (2005), pp. 653-668. IEEE.
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Bobby D. Bryant Ph.D. Alumni bdbryant [at] cse unr edu
Risto Miikkulainen Faculty risto [at] cs utexas edu
Kenneth Stanley Postdoctoral Alumni kstanley [at] cs ucf edu
Kenneth Stanley Postdoctoral Alumni kstanley [at] cs ucf edu
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