- What is the difference between an extensional and an intensional definition?
- Define the concept of feedforward in a neural network.
- Explain what role the weights play in a neural network. How is an input value propagated through the network?
- Briefly describe gradient descent.
- Briefly describe backpropagation.
- Describe the difference between a policy agent and a value agent.
- What is the difference between a tensor and an array? What benefit do we get from using tensors?
- What is a placeholder tensor?
- How does a Q-network update its internal approximation of the Q-values of a state-action function?
- What types of architectures qualify as deep Q-networks?
- Briefly describe the difference between a neural network that implements a Q-learning algorithm and a deep Q-network.
Hands-On Q-Learning with Python
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Hands-On Q-Learning with Python
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Overview of this book
Q-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI). It is one of the most popular fields of study among AI researchers.
This book starts off by introducing you to reinforcement learning and Q-learning, in addition to helping you become familiar with OpenAI Gym as well as libraries such as Keras and TensorFlow. A few chapters into the book, you will gain insights into model-free Q-learning and use deep Q-networks and double deep Q-networks to solve complex problems. This book will guide you in exploring use cases such as self-driving vehicles and OpenAI Gym’s CartPole problem. You will also learn how to tune and optimize Q-networks and their hyperparameters. As you progress, you will understand the reinforcement learning approach to solving real-world problems. You will also explore how to use Q-learning and related algorithms in scientific research. Toward the end, you’ll gain insight into what’s in store for reinforcement learning.
By the end of this book, you will be equipped with the skills you need to solve reinforcement learning problems using Q-learning algorithms with OpenAI Gym, Keras, and TensorFlow.
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Hands-On Q-Learning with Python
Table of Contents (14 chapters)
Preface
Brushing Up on Reinforcement Learning Concepts
Getting Started with the Q-Learning Algorithm
Setting Up Your First Environment with OpenAI Gym
Teaching a Smartcab to Drive Using Q-Learning
Section 2: Building and Optimizing Q-Learning Agents
Building Q-Networks with TensorFlow
Digging Deeper into Deep Q-Networks with Keras and TensorFlow
Section 3: Advanced Q-Learning Challenges with Keras, TensorFlow, and OpenAI Gym
Decoupling Exploration and Exploitation in Multi-Armed Bandits
Further Q-Learning Research and Future Projects
Assessments
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