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The Bandit and Reinforcement Learning

In the previous chapter, we introduced Machine Learning and the types of learning or training used in ML (Unsupervised Training, Supervised Training, Reinforcement Learning, Imitation Learning, and Curriculum Learning). As we discussed, the various forms of learning each have their own advantages and disadvantages. While ML using supervised training has been used successfully in games as far back as 20 years ago, it never really found any traction. It wasn't until the successful use of Reinforcement Learning was shown to be capable of playing classic Atari games and GO better than humans, that the interest for ML in games and simulations was rekindled. Now, RL is one of the hottest topics in ML research and is showing the potential for building some real continually learning AI. We will spend the bulk of this chapter understanding RL...

Key benefits

• Learn how to apply core machine learning concepts to your games with Unity

• Learn the Fundamentals of Reinforcement Learning and Q-Learning and apply them to your games

• Learn How to build multiple asynchronous agents and run them in a training scenario

Description

Unity Machine Learning agents allow researchers and developers to create games and simulations using the Unity Editor, which serves as an environment where intelligent agents can be trained with machine learning methods through a simple-to-use Python API. This book takes you from the basics of Reinforcement and Q Learning to building Deep Recurrent Q-Network agents that cooperate or compete in a multi-agent ecosystem. You will start with the basics of Reinforcement Learning and how to apply it to problems. Then you will learn how to build self-learning advanced neural networks with Python and Keras/TensorFlow. From there you move o n to more advanced training scenarios where you will learn further innovative ways to train your network with A3C, imitation, and curriculum learning models. By the end of the book, you will have learned how to build more complex environments by building a cooperative and competitive multi-agent ecosystem.

What you will learn

• Develop Reinforcement and Deep Reinforcement Learning for games.

• Understand complex and advanced concepts of reinforcement learning and neural networks

• Explore various training strategies for cooperative and competitive agent development

• Adapt the basic script components of Academy, Agent, and Brain to be used with Q Learning.

• Enhance the Q Learning model with improved training strategies such as Greedy-Epsilon exploration

• Implement a simple NN with Keras and use it as an external brain in Unity

• Understand how to add LTSM blocks to an existing DQN

• Build multiple asynchronous agents and run them in a training scenario

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

€41.99

Unity 2018 Artificial Intelligence Cookbook

€36.99

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

€24.99

Total € 103.97

Digital Kristen & Family Aug 04, 2018

While the information was great, attempting to get a windows machne to work with the examples was filled with headaches and errors after errors. I was unable to get any of the examples to work that used external trainers. If your looking for a book on theory, pick this book up else i wouldn't suggest this book.

Amazon Verified review

Crystal Shining Unicorn Princess Jul 31, 2018

Absolutely useless book covering what is actually written in free unity ML Agents tutorials (not even 100% of what is covered). Don't waste your money, invest in some legit books on machine learning.

rishabh Nov 24, 2018

Chapters have no good explanation of anything. I found better explanation of the ml-agents examples provided by unity on its own website. The last chapter felt like things were heading in a better direction. The author just gives a set of instructions to set up a project instead of explaining why he is using a specific piece of code and its impact on the project. A true let down.

Learn Unity ML-Agents ??? Fundamentals of Unity Machine Learning: Incorporate new powerful ML algorithms such as Deep Reinforcement Learning for games

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

The Bandit and Reinforcement Learning

Reinforcement Learning

Contextual bandits and state

Exploration and exploitation

MDP and the Bellman equation

Q-Learning and connected agents

Exercises

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Learn Unity ML-Agents ??? Fundamentals of Unity Machine Learning: Incorporate new powerful ML algorithms such as Deep Reinforcement Learning for games

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