Learn Unity ML-Agents - Fundamentals of Unity Machine Learning

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. 1: Introducing Machine Learning and ML-Agents Machine Learning ML-Agents Running a sample Creating an environment Academy, Agent, and Brain Summary 2: 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 Summary 3: Deep Reinforcement Learning with Python Installing Python and tools ML-Agents external brains Neural network foundations Deep Q-learning Proximal policy optimization Exercises Summary 4: Going Deeper with Deep Learning Agent training problems Convolutional neural networks Experience replay Partial observability, memory, and recurrent networks Asynchronous actor – critic training Exercises Summary 5: Playing the Game Multi-agent environments Adversarial self-play Decisions and On-Demand Decision Making Imitation learning Curriculum Learning Exercises Summary 6: Terrarium Revisited – A Multi-Agent Ecosystem What was/is Terrarium? Building the Agent ecosystem Basic Terrarium – Plants and Herbivores Carnivore: the hunter Next steps Exercises Summary