Book Image

Advanced Deep Learning with Keras

By : Rowel Atienza
Book Image

Advanced Deep Learning with Keras

By: Rowel Atienza

Overview of this book

Recent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Deep Reinforcement Learning (DRL) are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like. Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques. The journey begins with an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book. You’ll learn how to implement deep learning models with Keras and TensorFlow 1.x, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You then learn all about GANs, and how they can open new levels of AI performance. Next, you’ll get up to speed with how VAEs are implemented, and you’ll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of advanced techniques, you'll learn how to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.
Table of Contents (13 chapters)
12
Index

Monte Carlo policy gradient (REINFORCE) method

The simplest policy gradient method is called REINFORCE [5], this is a Monte Carlo policy gradient method:

Monte Carlo policy gradient (REINFORCE) method (Equation 10.2.1)

where Rt is the return as defined in Equation 9.1.2. Rt is an unbiased sample of Monte Carlo policy gradient (REINFORCE) method in the policy gradient theorem.

Algorithm 10.2.1 summarizes the REINFORCE algorithm [2]. REINFORCE is a Monte Carlo algorithm. It does not require knowledge of the dynamics of the environment (that is, model-free). Only experience samples, Monte Carlo policy gradient (REINFORCE) method, are needed to optimally tune the parameters of the policy network, Monte Carlo policy gradient (REINFORCE) method. The discount factor, Monte Carlo policy gradient (REINFORCE) method, takes into consideration that rewards decrease in value as the number of steps increases. The gradient is discounted by Monte Carlo policy gradient (REINFORCE) method. Gradients taken at later steps have smaller contributions. The learning rate, Monte Carlo policy gradient (REINFORCE) method, is a scaling factor of the gradient update.

The parameters are updated by performing gradient ascent using the discounted gradient and learning rate. As a Monte Carlo algorithm, REINFORCE requires...