This course teaches learners about Unity ML-Agents, an open-source toolkit that combines Unity and Machine Learning. The course covers the advances in AI, such as reinforcement and imitation learning, and how they can revolutionize game development. The skills taught include setting up games for training, training methods like reinforcement learning, and examples such as the Chicken Crossing the Road scenario. The teaching method involves a video presentation by experts in Deep Learning and Machine Learning. The intended audience for this course is individuals interested in the intersection of Unity game development and machine learning techniques.
Overview
Syllabus
Intro
Machine Learning for Gaming Al
Unity ML Agents Workflow
What are Agents?
How can agents be used in games?
Set Up Game for Training
Training Methods
Reinforcement Learning Process
Example: Chicken Crossing the Road
Included ML Agents Training Examples
3D Balance Ball
Curriculum Learning
Multi-Agent Soccer Training
Curiosity-Driven Exploration
Pyramids Environment
External reward only
Curiosity and external reward
Machine Learning Inference
Challenges of Inference - Computation Complexity
Challenges of Inference - Platforms to support
Unity Inference Solution
Unity Labs Inference Engine
Thank you!
Taught by
Unity
Tags
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