At this point in this book, we need to move beyond building toy examples and look to building modules or frameworks you can use to train your own agents in the future. In fact, we will use the code in this chapter for training agents to solve other challenge environments we present in later chapters. That means we need a more general way to capture our progress, preferably to log files that we can view later. Since building such frameworks is such a common task to machine learning as a whole, Google developed a very useful logging framework called TensorBoard. TensorBoard was originally developed as a subset of the other DL framework we mentioned earlier, TensorFlow. Fortunately, for us, PyTorch includes an extension that supports logging to TensorBoard. So, in this section, we are going to set up and install TensorBoard for use as a logging and graphing platform...
Hands-On Reinforcement Learning for Games
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Hands-On Reinforcement Learning for Games
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Overview of this book
With the increased presence of AI in the gaming industry, developers are challenged to create highly responsive and adaptive games by integrating artificial intelligence into their projects. This book is your guide to learning how various reinforcement learning techniques and algorithms play an important role in game development with Python.
Starting with the basics, this book will help you build a strong foundation in reinforcement learning for game development. Each chapter will assist you in implementing different reinforcement learning techniques, such as Markov decision processes (MDPs), Q-learning, actor-critic methods, SARSA, and deterministic policy gradient algorithms, to build logical self-learning agents. Learning these techniques will enhance your game development skills and add a variety of features to improve your game agent’s productivity. As you advance, you’ll understand how deep reinforcement learning (DRL) techniques can be used to devise strategies to help agents learn from their actions and build engaging games.
By the end of this book, you’ll be ready to apply reinforcement learning techniques to build a variety of projects and contribute to open source applications.
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Current Title:
Hands-On Reinforcement Learning for Games
Table of Contents (19 chapters)
Preface
Section 1: Exploring the Environment
Free Chapter
Understanding Rewards-Based Learning
Dynamic Programming and the Bellman Equation
Monte Carlo Methods
Temporal Difference Learning
Exploring SARSA
Section 2: Exploiting the Knowledge
Going Deep with DQN
Going Deeper with DDQN
Policy Gradient Methods
Optimizing for Continuous Control
All about Rainbow DQN
Exploiting ML-Agents
DRL Frameworks
Section 3: Reward Yourself
3D Worlds
From DRL to AGI
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