Google Wants to Enable Devs to Create WoW-Sized Games With Small Teams Through Machine Learning

Machine learning techniques are being applied to all sorts of technologies, and it'due south just natural to wonder how those could be used to empower game evolution.

That's exactly what Google is researching for developers through its Project Chimera. A team of engineers and developers have been looking into the potential applications of generative adversarial networks (GANs).

Speaking with MCVUK magazine (Apr 2020, effect 956), Erin Hoffman-John, head of creative for Stadia enquiry and development, explained that machine learning could allow small development teams to create fifty-fifty Earth of Warcraft-sized games. To begin with, content cosmos can be made much simpler as machine learning trains on a fix of reference images then produces completely new designs based on that style.

We're taking on the risk that developers don't desire to. Nosotros've been talking externally to developers and asking them, what are the things that you've e'er wanted to do only have not been able to exercise? What are the things that yous've had to cut out of your games because you haven't been able to do them fast enough, or y'all simply haven't had the processing power?

What if a team of 14 people could brand a game the scale of World of Warcraft? That'due south an absurd goal, right? The thing about games similar WoW is that they rely on a lot of heavy, repetitive content creation. The artists and the writers are doing a lot of essentially duplicate work, that's where a lot of the investment goes. If yous look at the amount of money that is spent making a game similar World Warcraft, it'due south like 70% content and xxx% or less code, even though it's a tremendous amount of code, it'south way more than on the content side.

This isn't unlike what nosotros've seen recently with NVIDIA's StyleGAN machine learning, where a generative adversarial network was used to recreate new manga designs based on the works of Osamu Tezuka.

Google's Projection Chimera also aims to make balancing much easier for game developers. This is in one case once more something that usually would exist very complex to do thoroughly for smaller teams, but machine learning (more specifically reinforcement learning) can get a long fashion to ready that, according to Erin Hoffman-John.

[...] by playing the game millions of times with reinforcement learning agents that nosotros've trained on the rules of the game, that lets u.s. test the balance very, very quickly. So fifty-fifty a pocket-size developer who might not have access to hundreds of people to playtest their game could have access to this reinforcement learning tool that will optimise the play of the game. It tin learn the game by itself without being scripted and so tell y'all where the issues are in the balancing. It lets you lot exam your theories of the design against what'southward actually happening in real time.

Again, there are already examples of reinforcement learning. For case, DeepMind'south AlphaStar AI is already capable of beating 99.eight% of Starcraft II human players. However, the goal here would be to help balance the game rather than merely utilise machine learning to shell humans. Additionally, for both techniques discussed, their effectiveness could very well vary between game types.

Still, it would be a significant advocacy if such large games could be successfully fabricated fifty-fifty past those studios that don't have thousands of developers at their disposal. Machine learning is certain to play an increasingly big role in gaming, and we'll proceed you upwardly to date on its applications.