This book introduces reinforcement learning with mathematical theory and practical examples
from quantitative finance using the TensorFlow library. Reinforcement Learning for Finance
begins by describing methods for training neural networks. Next it discusses CNN and RNN - two
kinds of neural networks used as deep learning networks in reinforcement learning. Further the
book dives into reinforcement learning theory explaining the Markov decision process value
function policy and policy gradients with their mathematical formulations and learning
algorithms. It covers recent reinforcement learning algorithms from double deep-Q networks to
twin-delayed deep deterministic policy gradients and generative adversarial networks with
examples using the TensorFlow Python library. It also serves as a quick hands-on guide to
TensorFlow programming covering concepts ranging from variables and graphs to automatic
differentiation layers models and loss functions. After completing this book you will
understand reinforcement learning with deep q and generative adversarial networks using the
TensorFlow library. What You Will Learn Understand the fundamentals of reinforcement learning
Apply reinforcement learning programming techniques to solve quantitative-finance problems Gain
insight into convolutional neural networks and recurrent neural networks Understand the Markov
decision process Who This Book Is ForData Scientists Machine Learning engineers and Python
programmers who want to apply reinforcement learning to solve problems.