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Game Engines and Machine Learning: Training a Self-Driving Car Without a Car?

YOW! Data 2019

Are you a scientist who wants to test a research problem without building costly and complicated real-world rigs? A self-driving car engineer who wants to test their AI logic in a constrained virtual world? A data scientist who needs to solve a thorny real-world problem without touching a production environment? Have you considered AI problem solving using game engines?

No? This session will teach you how to solve AI and ML problems using the Unity game engine, and Google’s TensorFlow for Python, as well as other popular ML tools.

In this session, we’ll show you ML and AI problem solving with game engines. Learn how you could use a game engine to train, explore, and manipulate intelligence agents that learn.

Game engines are a great place to explore ML and AI. They’re wonderful constrained problem spaces, tiny little ecosystems for you to explore a problem in. Here you can learn how to use them even though you’re not a game developer, with no game development experience required!

In this session, we’ll look at:

  • how video game engines are a perfect environment to constrain a problem and train an agent
  • how easy it is to get started, using the Unity engine and Google’s TensorFlow for Python
  • how to build up a model, and use it in the engine, to explore a particular idea or problem
  • PPO (proximal policy optimisation) for generic but useful machine learning
  • deep reinforcement learning, and how it lets you explore and study complex behaviours

Specifically, this session will:

  • teach the very basics of the Unity game engine
  • explore how to setup a scene in Unity for both training and use of a ML model
  • show how to train a model, using TensorFlow (and Docker), using the Unity scene
  • discuss the use of the trained model, and potential applications
  • show you how to train AI agents in complicated scenarios and make the real world better by leveraging the virtual

We’ll explore fun, engaging scenarios, including virtual self-driving cars, bipedal human-like walking robots, and disembodied hands that can play tennis.

This session is for non-game developers to learn how they can use game technologies to further their understanding of machine learning fundamentals, and solve problems using a combination of open source tools and (sadly often not open source) game engines. Deep reinforcement learning using virtual environments is the beginning of an exciting new wave of AI.

It’s a bit technical, a bit creative.