- Reward refers to the current point value of taking an action, and value refers to the overall utility of an agent's future actions as a result of taking that action.
- A hyperparameter value is not determined by anything in the model itself and has to be set externally. Some kinds of hyperparameters might be the depth of or number of leaf nodes on a decision tree model.
- Because we don't want a learning agent to keep taking the same high-valued actions over and over if there are higher-valued actions available, an exploration strategy has it take a random action with the goal of discovering actions that might be higher-valued than the ones we've already seen. It could be taking a random action as a result of an exploration strategy.
- In a situation where we would value future rewards more heavily at the beginning...
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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