Название: Reinforcement Learning for Finance: Solve Problems in Finance with CNN and RNN Using the TensorFlow LibraryАвтор: Samit Ahlawat
Издательство: Apress
Год: 2023
Страниц: 435
Язык: английский
Формат: pdf (true), epub (true)
Размер: 37.3 MB
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.
Neural network libraries like TensorFlow, PyTorch, and Caffe had made tremendous contributions in the rapid development, testing, and deployment of deep neural networks, but I found most applications restricted to computer science, computer vision, and robotics. Having to use reinforcement learning algorithms in finance served as another reminder of the paucity of texts in this field. Furthermore, I found myself referring to scholarly articles and papers for mathematical proofs of new reinforcement learning algorithms. This led me to write this book to provide a one-stop resource for Python programmers to learn the theory behind reinforcement learning, augmented with practical examples drawn from the field of finance.
In practical applications, reinforcement learning draws upon deep neural networks. To facilitate exposition of topics in reinforcement learning and for continuity, this book also provides an introduction to TensorFlow and covers neural network topics like convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
Finally, this book also introduces readers to writing modular, reusable, and extensible reinforcement learning code. Having worked on developing trading strategies using reinforcement learning and publishing papers, I felt existing reinforcement learning libraries like TF-Agents are tightly coupled with the underlying implementation framework and do not express central concepts in reinforcement learning in a manner that is modular enough for someone conversant with concepts to pick up TF-Agent library usage or extend its algorithms for specific applications. The code samples covered in this book provide examples of how to write modular code for reinforcement learning.
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 For
Data Scientists, Machine Learning engineers and Python programmers who want to apply reinforcement learning to solve problems.
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