Description
Book SynopsisThis 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, andloss functions. After completing this bo
Table of ContentsChapter 1 Overview 1.1 Methods for Training Neural Networks
Chapter 2 Convolutional Neural Networks 2.1 A Simple CNN 2.2 Identifying Technical Patterns in Security Prices
Chapter 3 Recurrent Neural Networks 3.1 LSTM Network 3.2 LSTM Application: Correlation in Asset Returns
Chapter 4 Reinforcement Learning 4.1 Basics 4.2 Methods For Estimating MDP 4.3 Value Estimation Methods 4.4 Policy Learning 4.5 Actor-Critic Algorithms 4.6; Implementation of algorithms to quantitative finance using TensorFlow - 1
Chapter 5 Recent Advances in Reinforcement Learning Algorithms 5.1 Double Deep Q-Network: DDQN 5.2 Dueling Double Deep Q-Network 5.3 Noisy Networks 5.4 Deterministic Policy Gradient
