Deep Reinforcement Learning for General Video Game AI

The realization that video games are perfect testbeds for artificial intelligence methods have in recent years spread to the whole AI community, in particular since Chess and Go have been effectively conquered, and there is an almost daily flurry of new papers applying AI methods to video games. In particular, the Arcade Learning Environment (ALE), which builds on an emulator for the Atari 2600 games console and contains several dozens of games [1], have been used in numerous published papers since DeepMind's landmark paper showing that Q-learning combined with deep convolutional networks could learn to play many of the ALE games at superhuman level [2]. As an AI benchmark, ALE is limited in the sense that there is only a finite set of games. This is a limitation it has in common with any framework based on existing published games. However, for being able to test the general video game playing ability of an agent, it is necessary to test on games on which the agent was not optimized.