Building, teaching, and training simulations for machine learning with a game engine
Who is this presentation for?
- Anyone interested in the future of ML, working with complicated physical-visual-cognitive ML problems, and interested in a new approach to solving ML problems or exploring the world of ML
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 the logic.
Enter game engines.
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 and Tim Nugent explore how you can use game engines to explore and solve problems—such as the aforementioned robot-driven warehouse—in a simulated environment without building costly and complicated real-world rigs. You’ll learn how straightforward a game engine can be; how to define and lay out your environment for ML problem solving; how to choose and apply different ML techniques, algorithms, and learning approaches, such as (deep) reinforcement learning, imitation learning, and curriculum learning; generic but powerful algorithms such as proximal policy optimization (PPO) that make this new wave of ML possible; how to tune simulations and pick the right actions, observations, and rewards to optimize your agents; and how to design for emergent problem solving by your agents, where you’re not quite sure what the optimal solution looks like. Using the popular, powerful Unity game engine, and its integration with TensorFlow as a case study, you’ll explore simulation-driven machine learning. The principles will be generally applicable to any simulation or game engine environment, and machine learning broadly.
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. Game engines are one of the most significant driving forces in leading-edge AI. Here you can learn how to use them even though you’re not a game developer. (It’s also a whole lot of fun.)
- A basic understanding of reinforcement learning (useful but not required)
What you'll learn
- Understand how and why to use simulations (via game engines) to solve ML problems
- Gain clear and concrete next steps to take the knowledge further
Paris Buttfield-Addison is a cofounder 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 the Yarn Spinner narrative game framework. Previously, Paris was a mobile product manager for Meebo (acquired by Google). Paris particularly enjoys game design, statistics, blockchain, 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 and is writing Practical AI with Swift and Head First Swift. He holds a degree in medieval history and a PhD in computing. You can find him on Twitter as @parisba.
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 writing Practical Artificial Intelligence with Swift for O’Reilly and building a game for a power transmission company about a naughty quoll. (A quoll is an Australian animal.)
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