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1. Understanding Rewards-Based Learning
2. Dynamic Programming and the Bellman Equation
3. Monte Carlo Methods
4. Temporal Difference Learning
5. Exploring SARSA
6. Going Deep with DQN
7. Going Deeper with DDQN
8. Policy Gradient Methods
9. Optimizing for Continuous Control
10. All about Rainbow DQN
11. Exploiting ML-Agents
12. DRL Frameworks
13. 3D Worlds
14. From DRL to AGI

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.