Simulation-driven artificial intelligence using a game engine
What you'll learn, and how you can apply it
The main takeaways will be:
- the potential of game engines for machine learning, in clear concrete ways
- how to use a game engine (generally), exploring Unity (specifically)
- how to use the open source TensorFlow and Unity ML-Agents Toolkits for machine learning with simulations
Who is this presentation for?
Anyone with an interested in applying or using AI for problem solving, getting into AI at all, learning how to use AI through a very visual means, or furthering their careers by simulating things. Any programmer or tech generalists.
Nothing special. Tech generalists will get a lot out of it. If you know basic AI principles (agents, actions, training) or the basics of any 3D application/3D toolkit, you'll get up to speed quicker.
Hardware and/or installation requirements:
We'll provide a GitHub repository and everything that's needed to install on macOS, Windows, or Linux. Attendees should install the free Unity game engine in advance (steps will be provided).
Whether you’re a data 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 your AI logic in a constrained virtual world, or a data scientist who needs to solve a thorny real-world problem without a production environment, simulation-driven ML problem solving with a game engine might be the solution to your problems. Imagine you’re building a high-volume, expensive, robot-driven warehouse.
Your pick, place, and packing robots need to get to the right place quickly, find the right item, and sort it to the right place without colliding with each other, the shelves, or people. But you don’t have any robots yet, and you need to start writing, or training, the behavior.
A game engine is a controlled, self-contained spatial, physical environment that (can) closely replicate (enough of) the real world (to be useful). Paris Buttfield-Addison, Tim Nugent, and Mars Geldard explore how you can use game engines to explore and solve problems—such as the aforementioned warehouse—in a simulated environment without building costly and complicated real-world rigs. They detail how to solve AI and ML problems using the Unity game engine (not open source), TensorFlow for Python (open source), and the Unity ML-Agents Toolkit (open source). Learn how you could use a game engine to train, explore, and manipulate intelligent agents that learn, explore, and help you understand a problem space.
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 to use them, even though you’re not a game developer.
In this training, you’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 (not open source) and Google’s TensorFlow for Python (very open source)
- How to build up a model, and use it in the engine, to explore a particular idea or problem
- Proximal policy optimization (PPO) for generic but useful machine learning
- Deep reinforcement learning and how it lets you explore and study complex behaviors
Specifically, Paris, Tim, and Mars will
- Spend 1/3 of the day teaching you the very basics of a game engine, using the popular (and free) Unity game engine as a case study
- Spend the rest of the day exploring how to design and set up a scene in Unity for both training and use of a ML model and how to pick the right actions, observations, and rewards for training
- Show you how to train a model using TensorFlow and using the Unity scene; the difference between imitation learning, reinforcement learning, and other approaches; and how to train AI agents in complicated scenarios and make the real world better by leveraging the virtual
- Discuss the use of the trained model and potential applications
You’ll explore fun, engaging scenarios, including virtual self-driving cars, bipedal human-like walking robots, disembodied hands that can play tennis, and packing a warehouse efficiently using robots.
Non-game developers can learn how to use game technologies to further their understanding of machine learning fundamentals and solve problems using a combination of open source ML tools and game engines. Deep reinforcement learning using virtual environments is the beginning of an exciting new wave of AI, and game engines are on the leading edge. You’ll learn how to approach your ML work in a totally new way.
You’ll be using Unity, TensorFlow, and related tools, but all the principles and skills are general and transferrable to other game engines or simulation platforms (e.g., Open AI and the like).
It’s a bit technical, a bit creative, and you’ll leave with brand-new skills and an enthusiasm for using them. You’ll get your their own trained virtual robot (or car, or animal, or…anything you can imagine).
About your instructors
Paris Buttfield-Addison is a co-founder of Secret Lab, a game development studio based in beautiful Hobart, Australia. Secret Lab builds games and game development tools, including the multi-award-winning ABC Play School iPad games, the BAFTA- and IGF-winning Night in the Woods, the Qantas airlines Joey Playbox games, and popular open source Yarn Spinner narrative game framework. Previously, Paris was a mobile product manager for Meebo (acquired by Google). Paris particularly enjoys game design, statistics, law, machine learning, and human-centered technology. He researches and writes technical books on mobile and game development (more than 20 so far) for O’Reilly, recently releasing Practical AI with Swift, and currently writing Head First Swift. He holds a degree in medieval history and a PhD in computing. You can find him on Twitter as @parisba.
Marina (Mars) Rose Geldard is a researcher from Down Under in Tasmania. Entering the world of technology relatively late as a mature-age student, she’s found her place in the world: an industry where she can apply her lifelong love of mathematics and optimization. When she’s not busy being the most annoyingly eager researcher ever, she compulsively volunteers at industry events, dabbles in research, and serves on the executive committee for her state’s branch of the Australian Computer Society (ACS). She’s recently worked at Canva, Australia’s hottest startup, doing data science, and also released Practical Artificial Intelligence with Swift for O’Reilly Media.
Tim Nugent pretends to be a mobile app developer, game designer, tools builder, researcher, and tech author. When he isn’t busy avoiding being found out as a fraud, Tim spends most of his time designing and creating little apps and games he won’t let anyone see. He also spent a disproportionately long time writing his tiny little bio, most of which was taken up trying to stick a witty sci-fi reference in…before he simply gave up. He’s recently released Practical Artificial Intelligence with Swift for O’Reilly, and built a game for a power transmission company about a naughty quoll. (A quoll is an Australian animal.) He’s currently working on Yarn Spinner, the open source narrative game development framework.
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