As you may have surmised by now, writing your own RL algorithms and functions on top of a deep learning framework, such as PyTorch, is not trivial. It is also important to remember that the algorithms in this book go back about 30 years over the development of RL. That means that any serious new advances in RL take substantial effort and time—yes, for both development and especially training. Unless you have the time, resources, and incentive for developing your own framework, then it is highly recommended to graduate using a mature framework. However, there is an ever-increasing number of new and comparable frameworks out there, so you may find that you are unable to choose just one. Until one of these frameworks achieves true AGI, then you may also need separate frameworks for different environments or even different tasks.
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.
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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