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Learn Unity ML-Agents ??? Fundamentals of Unity Machine Learning

You're reading from Learn Unity ML-Agents ??? Fundamentals of Unity Machine Learning Incorporate new powerful ML algorithms such as Deep Reinforcement Learning for games

Product type Paperback

Published in Jun 2018

Publisher Packt

ISBN-13 9781789138139

Length 204 pages

Edition 1st Edition

Languages

C#

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

Micheal Lanham

View More author details

Table of Contents (8) Chapters

Preface

1. Introducing Machine Learning and ML-Agents

2. The Bandit and Reinforcement Learning FREE CHAPTER

3. Deep Reinforcement Learning with Python

4. Going Deeper with Deep Learning

5. Playing the Game

6. Terrarium Revisited – A Multi-Agent Ecosystem

7. Other Books You May Enjoy

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Exercises

Complete the following exercises on your own:

Alter the GridWorldBrain parameters in the trainer_config.yaml file and run further training sessions to explore the effect of changing the parameters.
Build the 3DBalls environment and train it with the learn.py PPO algorithm using an external brain.
Alter the parameters of the trainer_config.yaml file for the Ball3DBrain, run the simulation again, and view the results with TensorBoard.

Be sure to take some time to run through a few examples and configure some of the training parameters. Understanding what effect these hyperparameters can have on the impact of model training can be critical for you to successfully train working models.

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