A Neuroevolution Approach to General Atari Game Playing

Matthew Hausknecht, Joel Lehman, Risto Miikkulainen, Peter Stone
2014 IEEE Transactions on Computational Intelligence and AI in Games  
This article addresses the challenge of learning to play many different video games with little domainspecific knowledge. Specifically, it introduces a neuro-evolution approach to general Atari 2600 game playing. Four neuro-evolution algorithms were paired with three different state representations and evaluated on a set of 61 Atari games. The neuro-evolution agents represent different points along the spectrum of algorithmic sophistication -including weight evolution on topologically fixed
more » ... al networks (Conventional Neuro-evolution), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), evolution of network topology and weights (NEAT), and indirect network encoding (HyperNEAT). State representations include an object representation of the game screen, the raw pixels of the game screen, and seeded noise (a comparative baseline). Results indicate that direct-encoding methods work best on compact state representations while indirect-encoding methods (i.e. HyperNEAT) allow scaling to higher-dimensional representations (i.e. the raw game screen). Previous approaches based on temporaldifference learning had trouble dealing with the large state spaces and sparse reward gradients often found in Atari games. Neuro-evolution ameliorates these problems and evolved policies achieve state-of-the-art results, even surpassing human high scores on three games. These results suggest that neuro-evolution is a promising approach to general video game playing.
doi:10.1109/tciaig.2013.2294713 fatcat:nbiiajg5pfcijanbks2nscldxa