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Intro
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Machine Learning for Gaming Al
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Unity ML Agents Workflow
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What are Agents?
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How can agents be used in games?
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Set Up Game for Training
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Training Methods
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Reinforcement Learning Process
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Example: Chicken Crossing the Road
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Included ML Agents Training Examples
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3D Balance Ball
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Curriculum Learning
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Multi-Agent Soccer Training
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Curiosity-Driven Exploration
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Pyramids Environment
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External reward only
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Curiosity and external reward
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Machine Learning Inference
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Challenges of Inference - Computation Complexity
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Challenges of Inference - Platforms to support
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Unity Inference Solution
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Unity Labs Inference Engine
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Thank you!
Description:
Explore the world of machine learning in game development through this 42-minute conference talk from Unite LA. Discover Unity ML-Agents, an open-source toolkit bridging Unity and machine learning. Learn about recent advances in AI, including reinforcement and imitation learning, and their potential to revolutionize game production. Gain insights into the ML-Agents workflow, understand what agents are and how they can be used in games, and explore various training methods. Follow along with practical examples like the 3D Balance Ball, Multi-Agent Soccer Training, and Curiosity-Driven Exploration. Delve into the challenges of machine learning inference, including computational complexity and platform support, and learn about Unity's innovative solutions. Presented by Arthur Juliani and Vladimir Oster from Unity Technologies, this talk offers a comprehensive overview of how to democratize machine learning in game development.

Democratize Machine Learning - ML-Agents Explained

Unity
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