Whether you’re 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 your AI logic in a constrained virtual world, or a data scientist who needs to solve a thorny real-world problem without touching a production environment, AI problem solving using game engines might just be for you.
Games are wonderful contained problem spaces, making them great places to explore AI—even if you’re not a game developer. Paris Buttfield-Addison, Mars Geldard, and Tim Nugent teach you how to solve AI and ML problems using the Unity game engine and Google’s TensorFlow for Python to train, explore, and manipulate intelligent agents that learn.
Specifically, Paris, Mars, and Tim:
They’ll explore fun, engaging scenarios, including virtual self-driving cars, bipedal human-like walking robots, and disembodied hands that can play tennis, for nongame developers to learn how to 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.
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; he recently finished writing Practical AI with Swift and is currently working on Head First Swift. He holds a degree in medieval history and a PhD in computing. Paris loves to bring machine learning into the world of practical and useful. You can find him on Twitter as @parisba.
Marina Rose Geldard (Mars) is a technologist from Down Under in Tasmania. Entering the world of technology relatively late as a mature-age student, she has found her place in the world: an industry where she can apply her lifelong love of mathematics and optimization. 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) as well as the AUC. She’s writing Practical Artificial Intelligence with Swift for O’Reilly and working on machine learning projects to improve public safety through public CCTV cameras in her hometown of Hobart.
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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We’re excited to see everyone tomorrow!
Just a reminder to install the perquisites if you have chance! It will mean you can get straight into it at OSCON!
Details are here: https://secretlab.institute/2019/07/11/installing-unity-ml-agents/
Please post if you have any questions.
If you don’t have time, or can’t manage to make it work, please still come along and we’ll figure it out with you on site!
(@parisba on Twitter)
The documentation was corrected shortly after original posting to read ‘pip install tensorflow==1.7.1’ on that line. If you run into any further troubles, we’re continuing to update the post—as we double-check the activities on different machines and setups and with different people—to include any troubles or ambiguities we encounter.
Apologies for the inconvenience.
Installing on a MacBook Pro using the instructions athttps://secretlab.institute/2019/07/11/installing-unity-ml-agents/ Running into some trouble:
(UnityML) HISJMEN36:~ ssmith$ pip install tensorflow1.71 Collecting tensorflow1.71
ERROR: Could not find a version that satisfies the requirement tensorflow1.71 (from versions: 0.12.1, 1.0.0, 1.1.0rc0, 1.1.0rc1, 1.1.0rc2, 1.1.0, 1.2.0rc0, 1.2.0rc1, 1.2.0rc2, 1.2.0, 1.2.1, 1.3.0rc0, 1.3.0rc1, 1.3.0rc2, 1.3.0, 1.4.0rc0, 1.4.0rc1, 1.4.0, 1.4.1, 1.5.0rc0, 1.5.0rc1, 1.5.0, 1.5.1, 1.6.0rc0, 1.6.0rc1, 1.6.0, 1.7.0rc0, 1.7.0rc1, 1.7.0, 1.7.1, 1.8.0rc0, 1.8.0rc1, 1.8.0, 1.9.0rc0, 1.9.0rc1, 1.9.0rc2, 1.9.0, 1.10.0rc0, 1.10.0rc1, 1.10.0, 1.10.1, 1.11.0rc0, 1.11.0, 1.12.0rc0, 1.12.0rc1, 1.12.0rc2, 1.12.0, 1.12.2, 1.12.3, 1.13.0rc0, 1.13.0rc1, 1.13.0rc2, 1.13.1, 1.14.0rc0, 1.14.0rc1, 1.14.0, 2.0.0a0, 2.0.0b0, 2.0.0b1) ERROR: No matching distribution found for tensorflow1.71
If not 1.71 which version should we use? I tried not specifying the version and it loaded the latest. That was fine but pip install mlagents expects tensorflow 1.71:
ERROR: Could not find a version that satisfies the requirement tensorflow1.7.1 (from mlagents) (from versions: 1.13.0rc1, 1.13.0rc2, 1.13.1, 1.14.0rc0, 1.14.0rc1, 1.14.0, 2.0.0a0, 2.0.0b0, 2.0.0b1) ERROR: No matching distribution found for tensorflow1.7.1 (from mlagents)
If you’re attending this session, please check out this guide to installing the prerequisites:
Post here if you have any questions.