Real-time interactive learning in the NERO video game

摘要

In the NeuroEvolving Robotic Operatives (NERO) video game, the player trains a team of virtual robots for combat against other players ’ teams. The virtual robots learn in real time through interacting with the player. Since NERO was originally released in June, 2005, it has been downloaded over 50,000 times, ap-peared on Slashdot, and won several honors. The real-time NeuroEvolution of Augmenting Topologies (rt-NEAT) method, which can evolve increasingly complex artificial neural networks in real time as a game is being played, drives the robots ’ learning, making possible this entirely new genre of video game. The live demo will show how agents in NERO adapt in real time as they interact with the player. In the future, rtNEAT may al-