Google DeepMind took a leap forward last year when its artificial intelligence agent mastered 49 Atari 2600 games. The learning system, or "deep Q-network" (DQN), that DeepMind designed achieved this mastery through general experience, rather than specific programming for each game. This milestone is just one step along a grander path toward the general-purpose "smart machine": an AI that can master any task with minimal input. DeepMind's work in this field is groundbreaking, and it's helping advance the field in ways you might not expect.
Wojciech Jaśkowski is an assistant professor for the Institute of Computing Science (ICS) at Poznan University of Technology, Poland. After reading about DeepMind's feat in the scientific journal Nature, he began to think about the possibilities. If an agent could learn Atari 2600 with our current levels of knowledge, why not push the envelope further? Why not try a 3D game? Jaskowski settled on the 1993 first-person shooter Doom. It has low power requirements and, more important, it's open source. He assembled a team of university students from ICS with the aim of building a platform that would facilitate testing AI agents.
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