- You can do this by cloning the source instead of installing the package from pip. Further instructions are available in the Gym documentation.
- The term state is commonly used in the terminology of solving Markov decision processes, and the term observation is often used when describing RL environment state spaces. Both terms are equivalent in this context.
- Calling env.reset() resets the environment's state and returns the environment's current observation or state variable.
- The task will end when the done variable is set to True or the reward is set to 20, depending on your implementation. Both conditions indicate that the task has been solved.
- Setting env.s will manually change the state of the environment. This is bad practice when implementing an RL strategy; the state should not be set manually when solving...
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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