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...
Learn Unity ML-Agents ??? Fundamentals of Unity Machine Learning
Learn Unity ML-Agents ??? Fundamentals of Unity Machine Learning
Overview of this book
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.
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Current Title:
Learn Unity ML-Agents ??? Fundamentals of Unity Machine Learning
Table of Contents (8 chapters)
Preface
Free Chapter
Introducing Machine Learning and ML-Agents
The Bandit and Reinforcement Learning
Deep Reinforcement Learning with Python
Going Deeper with Deep Learning
Playing the Game
Terrarium Revisited – A Multi-Agent Ecosystem
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