This document discusses machine learning and how it can be used with Unity. It covers what machine learning can do using examples like data science and reinforcement learning. It explains how Unity can be used as an environment to observe and receive actions from machine learning models, and also to run trained models using TensorFlowSharp. It provides technical details on neural networks and model training. Finally, it recommends resources for learning more about machine learning and getting started with tools like OpenAI Gym and Unity.

1. Machine Learning & Unity
Andre Odendaal
http://omgsoftware.co.za
@Fengol

2. What I’m going to cover
As non-technical as possible
• What can Machine Learning do
• How does Unity fit in
• The technical stuff
• Where to get started
• Questions
• Goal: What is machine learning

3. What can Machine Learning do?
Data Science!

4. Real Life Example

5. OpenAI Gym
• OpenAI Gym is a toolkit for
developing and comparing
reinforcement learning
algorithms
• http://gym.openai.com/envs

6. Reinforcement Learning

7. How does Unity fit in
1. Unity is an environment we can observe
• Its runs the physics engine and outputs the observation or state and reward
• Its accepts our action
• The internal state developed in Python and UnityEnvironment is a
library for hosting games, retrieving observations and submitting
actions
2. Unity can run a trained model
• Unity uses TensorFlowSharp to run trained models

8. Demo UnityEnvironment
Recreating OpenAI’s FrozenLake in Unity (with Santa!)

9. Brain Neurons & Dial Boards

10. Neural Network

12. Technical Stuff
• A neuron is a number
between 0 and 1
• A neural layer is an array of
neurons (called a tensor)
• A neural network is an array
of arrays of arrays of … (also
called a tensor)
• Machine Learning is tuning
the values of the neurons
(tensor computation)

13. Underpants Gnomes Explanation

14. Technical Stuff cont.
• Tuning is done with
Regression, modelling the
relationship between
independent data (input) and
dependent data (output)
• We compare the output of
the neural network with an
expected value (called a loss
function) and use the
difference to update the
neurons.

15. Demo Model Training
Let the machine play instead of the humans

16. Where to get started
• Watch 3Blue1Brown’s videos on the subject
• https://www.youtube.com/watch?v=aircAruvnKk
• Install Python and TensorFlow
• https://www.python.org
• https://www.tensorflow.org
• Experiment using OpenAI Gym
• http://gym.openai.com/envs
• Create custom environments in Unity
• https://unity3d.ai

17. Further Reading
• Master Machine Learning Algorithms
• Jason Brownlee
• http://machinelearningmastery.com
• Data Science from Scratch
• Joel Grus
• http://joelgrus.com
• Simple Reinforcement Learning with Tensorflow series
• Arthur Juliani
• https://medium.com/emergent-future/simple-reinforcement-
learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-
networks-d195264329d0

18. Questions
and thank you for listening!