How The Brightest Minds In AI Are Using Gaming to Reinforce Learning


Kurzweil’s Law Of Accelerating Returns can be summed up by saying “Humans use the best tools (technology) available to create the next generation of technology”, enabling exponential technological improvement. Open Source projects and the proliferation of knowledge sharing continue to accelerate these effects. We’re excited and inspired by some of these recent developments in gaming and AI!

Here are a few interesting projects at the intersection of AI and Gaming:

Earlier this summer, Microsoft announced that Project Malmo, a Minecraft-based testing ground for Artificial Intelligence, has made their open-source code available on github. This project is an effort to help researchers learn more AI tasks, using reinforcement learning. Another interesting aspect of the public project is the creation of bots that can talk to each other and to humans.

No Man’s Sky, a long-awaited and hyped game known for being procedurally generated by an algorithm, not written by a human, is stealing many precious hours from excited gamers this week. (Trust me, my husband and MLconf Co-Founder must have dedicated half of last weekend to this game!) After years of hype and anticipation, the game is now available on Playstation 4 and Microsoft Windows.  Check out this article in the Atlantic, which paints a picture of the complexity of the game that generates full planets once a human is playing there.  

ViZDoom is a Doom-based AI research platform for reinforcement learning from raw visual information. It allows developing AI bots that play Doom using only the screen buffer. ViZDoom is primarily intended for research in machine visual learning, and deep reinforcement learning, in particular.

Google DeepMind, known for conquering Lee Sedol at AlphaGo, and creating an algorithm that can learn to play 49 arcade games, has also been chinking away at “Montezuma’s Revenge” with 50+million frames under their belt. In their recent research paper, the group of contributors explain that the winning ingredient in their success in this project was employing intrinsic motivation, in which they fed the exploration system digital rewards, similar to the human experience of  adrenaline, which encouraged the system to explore more frames, thus being more successful.

*Side Note: Ali Eslami, one of the authors of another popular paper by Google DeepMind researchers, Attend, Infer, Repeat: Fast Scene Understanding with Generative Models will be presenting at MLconf SF on 11/11/16, don’t miss it! Mention “Ali18” and save on tickets.

Upcoming MLconf Atlanta speaker, Chris Fregly, Co-Founder at PipelineIO, and his team have been helping a few large gaming companies apply their massive datasets towards more ML/AI use cases including the following:

  • Level-up recommendations:  ie. who they should fight next to reach the next level?
  • In-game purchase recommendations:  ie. extra weapons, health, food
  • Chat-log NLP analysis between players:  ie. prevent bullying and improve player engagement/retention
  • Cheat-pattern classification analysis:  ie. prevent online gambling fraud

Although the technology is evolving so quickly, it’s still not a perfect science. According to Fregly, the problem of labeling still exists.. At this point, it still takes a human to identify cheating/not cheating in gamer’s behavior.

As mentioned before, we’re in a time of enormous acceleration and it’s exciting! Gaming is just one field in which development and learning in artificial intelligence is improving. We’re excited to have some of these leaders involved in the MLconf community and we anticipate additional blog posts to follow as this work evolves.


Courtney Burton is the Founder of Sessions Events and MLconf- The Machine Learning Conference.