Reinforcement learning, an AI training technique that employs rewards to drive software policies toward goals, has been applied successfully to domains from industrial robotics to drug discovery. But while firms including OpenAI and Alphabet's DeepMind have investigated its efficacy in video games like Dota 2, Quake III Arena, and StarCraft 2, few to date have studied its use under constraints like those encountered in the game industry. That's presumably why Ubisoft La Forge, game developer Ubisoft's eponymous prototyping space, proposed in a recent paper an algorithm that's able to handle discrete, continuous video game actions in a "principled" and predictable way. They set it loose on a "commercial game" (likely The Crew or The Crew 2, though neither is explicitly mentioned) and report that it's competitive with state-of-the-art benchmark tasks. "Reinforcement Learning applications in video games have recently seen massive advances coming from the research community, with agents trained to play Atari games from pixels or to be competitive with the best players in the world in complicated imperfect information games," wrote the coauthors of a paper describing the work.

The most important part, though, is hidden in "the Minority Report of programming." The studio's Commit Assistant is the project of La Forge, the Ubisoft Montreal collaborative R&D division, and it is something truly amazing. When a programmer writes a new line of code, Commit Assistant searches for parts of the code that are adding bugs. The algorithm is based on a decade's worth of code changes and bug reports from Ubisoft games at every stage of development. The project is said to analyze bugs and create a "signature" that estimates the impact of the new code and the probability of future problems.