Handson Reinforcement Learning For Games Implementing Selflearning Agents In Games Using Artificial Intelligence Techniques 1st Edition Micheal Lanham by Micheal Lanham 9781839214936, 1839214937 instant download after payment.

Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlow

• Get to grips with the different reinforcement and DRL algorithms for game development
• Learn how to implement components such as artificial agents, map and level generation, and audio generation
• Gain insights into cutting-edge RL research and understand how it is similar to artificial general research

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.

• Understand how deep learning can be integrated into an RL agent
• Explore basic to advanced algorithms commonly used in game development
• Build agents that can learn and solve problems in all types of environments
• Train a Deep Q-Network (DQN) agent to solve the CartPole balancing problem
• Develop game AI agents by understanding the mechanism behind complex AI
• Integrate all the concepts learned into new projects or gaming agents

If you’re a game developer looking to implement AI techniques to build next-generation games from scratch, this book is for you. Machine learning and deep learning practitioners, and RL researchers who want to understand how to use self-learning agents in the game domain will also find this book useful. Knowledge of game development and Python programming experience are required.

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