Hands-On Reinforcement Learning for Games

Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch OpenAI Gym and TensorFlowKey FeaturesGet to grips with the different reinforcement and DRL algorithms for game developmentLearn how to implement components such as artificial agents map and level generation and audio generationGain insights into cutting-edge RL research and understand how it is similar to artificial general researchBook DescriptionWith 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.What you will learnUnderstand how deep learning can be integrated into an RL agentExplore basic to advanced algorithms commonly used in game developmentBuild agents that can learn and solve problems in all types of environmentsTrain a Deep Q-Network (DQN) agent to solve the CartPole balancing problemDevelop game AI agents by understanding the mechanism behind complex AIIntegrate all the concepts learned into new projects or gaming agentsWho this book is forIf 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. About the Author Micheal Lanham is a proven software and tech innovator with 20 years of experience. During that time he has developed a broad range of software applications in areas including games graphics web desktop engineering artificial intelligence GIS and machine learning applications for a variety of industries as an R&D developer. At the turn of the millennium Micheal began working with neural networks and evolutionary algorithms in game development. He was later introduced to Unity and has been an avid developer consultant manager and author of multiple Unity games graphic projects and books ever since.