Book Image

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

Book Image

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.
Table of Contents (8 chapters)

Proximal policy optimization

Thus far, our discussion of RL has looked at simpler techniques for building agents with bandits and Q-learning. Q-learning is a popular algorithm, and as we learned, deep Q neural networks provide us with a great foundation to use to solve more difficult problems, such as a cart balancing a pole. The following table summarizes the various RL algorithms, what conditions they are capable of working in, and how they function:

Algorithm Model Policy Action Observation Operator
Q-Learning Model-free Off-policy Discrete Discrete Q value
SARSA – State Action Reward State Action Model-free On-policy Discrete Discrete Q value
DQN Deep Q Network Model-free Off-policy Discrete Continuous Q value
DDPG Deep Deterministic Policy Gradient Model-free Off-policy Continuous Continuous Q value
TRPO Trust Region Policy Optimization...