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)

Double Q-Learning (DDQN)

In DQN, the target Q-Network selects and evaluates every action resulting in an overestimation of Q value. To resolve this issue, DDQN [3] proposes to use the Q-Network to choose the action and use the target Q-Network to evaluate the action.

In DQN as summarized by Algorithm 9.6.1, the estimate of the Q value in line 10 is:

Double Q-Learning (DDQN)

Qtarget chooses and evaluates the action a j+1.

DDQN proposes to change line 10 to:

Double Q-Learning (DDQN)

The term Double Q-Learning (DDQN) lets Q to choose the action. Then this action is evaluated by Qtarget.

In Listing 9.6.1, both DQN and DDQN are implemented. Specifically, for DDQN, the modification on the Q value computation performed by get_target_q_value() function is highlighted:

# compute Q_max
# use of target Q Network solves the non-stationarity problem
def get_target_q_value(self, next_state):
    # max Q value among next state's actions
    if self.ddqn:
        # DDQN
        # current Q Network selects the action
        # a'_max = argmax_a' Q(s', a&apos...