- Generally speaking, a control process is designed to optimize a value or a set of values within a set of limitations.
- A Markov chain does not incorporate actions or rewards; it only has states and events that will lead from one state to the next.
- The Markov property is the certainty that knowledge of a system's future states does not depend on knowledge of past states, but only on the current state.
- The Taxi-v2 environment has 500 states based on the values the state variables can take. State variables are the location of the taxi, the location of the destination, and the location of the passenger.
- We include these states for simplicity in enumerating the state space. They are unreachable in the task because, in some cases, the environment reaches a terminal state before they can be reached. For example, when the taxi...
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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.
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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