Esports: Artificial Intelligence (Ai)
Books - eBooks
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Hands-On Reinforcement Learning for Games by
Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlow Key Features 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 Book Description 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. What you will learn 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 Who this book is for 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.
Call Number: eBookISBN: 9781839214936Publication Date: 2020-01-03
Practical Game AI Programming by
Jump into the world of Game AI developmentAbout This Book* Move beyond using libraries to create smart game AI, and create your own AI projects from scratch* Implement the latest algorithms for AI development and in-game interaction* Customize your existing game AI and make it better and more efficient to improve your overall game performanceWho This Book Is ForThis book is for game developers with a basic knowledge of game development techniques and some basic programming techniques in C# or C++.What You Will Learn* Get to know the basics of how to create different AI for different type of games* Know what to do when something interferes with the AI choices and how the AI should behave if that happens* Plan the interaction between the AI character and the environment using Smart Zones or Triggering Events* Use animations correctly, blending one animation into another and rather than stopping one animation and starting another* Calculate the best options for the AI to move using Pruning Strategies, Wall Distances, Map Preprocess Implementation, and Forced Neighbours* Create Theta algorithms to the AI to find short and realistic looking paths* Add many characters into the same scene and make them behave like a realistic crowdIn DetailThe book starts with the basics examples of AI for different game genres and directly jumps into defining the probabilities and possibilities of the AI character to determine character movement. Next, you'll learn how AI characters should behave within the environment created.Moving on, you'll explore how to work with animations. You'll also plan and create pruning strategies, and create Theta algorithms to find short and realistic looking game paths. Next, you'll learn how the AI should behave when there is a lot of characters in the same scene.You'll explore which methods and algorithms, such as possibility maps, Forward Chaining Plan, Rete Algorithm, Pruning Strategies, Wall Distances, and Map Preprocess Implementation should be used on different occasions. You'll discover how to overcome some limitations, and how to deliver a better experience to the player. By the end of the book, you think differently about AI.Style and approachThe book has a step-by-step tutorial style approach. The algorithms are explained by implementing them in #.
Call Number: eBookISBN: 9781787122819Publication Date: 2017-06-30
Hands-On Artificial Intelligence with Unreal Engine by
Learn to build intelligent and responsive Non-Player Characters for your games with Unreal Engine Game AI. Key Features Understand the built-in AI systems in Unreal Engine for building intelligent games Leverage the power of Unreal Engine 4 programming to create game AI that focuses on motion, animation, and tactics Learn to profile, visualize, and debug your Game AI for checking logic and optimizing performance Book Description Learning how to apply artificial intelligence ( AI ) is crucial and can take the fun factor to the next level, whether you're developing a traditional, educational, or any other kind of game. If you want to use AI to extend the life of your games and make them challenging and more interesting, this book is for you. The book starts by breaking down AI into simple concepts to get a fundamental understanding of it. Using a variety of examples, you will work through actual implementations designed to highlight key concepts and features related to game AI in UE4. You will learn to work through the built-in AI framework in order to build believable characters for every game genre (including RPG, Strategic, Platform, FPS, Simulation, Arcade, and Educational). You will learn to configure the Navigation, Environmental Querying, and Perception systems for your AI agents and couple these with Behavior Trees, all accompanied with practical examples. You will also explore how the engine handles dynamic crowds. In the concluding chapters, you will learn how to profile, visualize, and debug your AI systems to correct the AI logic and increase performance. By the end of the book, your AI knowledge of the built-in AI system in Unreal will be deep and comprehensive, allowing you to build powerful AI agents within your projects. What you will learn Get an in-depth knowledge about all the AI Systems within Unreal Engine Create complex AIs, understanding the art of designing and developing Behavior Tree Learn how to perform Environmental Queries (EQS) Master the Navigation, Perception, and Crowd Systems Profile and Visualize the AI Systems with powerful debugging tools Extend every AI and Debug system with custom nodes and functions Who this book is for Hands-On Artificial Intelligence with Unreal Engine is for you if you are a game developer with a bit experience in Unreal Engine, and now want to understand and implement believable game AI within Unreal Engine. The book will be both in Blueprint and C++, allowing people from every background to enjoy the book. Whether you're looking to build your first game or expand your knowledge to the edge as a Game AI Programmer, you will find plenty of exciting information and examples of game AI in terms of concepts and implementation, including how to extend some of these systems.
Call Number: eBookISBN: 9781788835657Publication Date: 2019-04-25
Media
Off Campus access username and password can be found in Blackboard on the
Institution Page. Students: look for Student Tools to Stay Connected at Atlantic Cape. Faculty: look for Key information about Libraries and Tutoring. Or you can contact the library for username and password.
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- OpenAi FiveAt OpenAI, we’ve used the multiplayer video game Dota 2 as a research platform for general-purpose AI systems. Our Dota 2 AI, called OpenAI Five, learned by playing over 10,000 years of games against itself. It demonstrated the ability to achieve expert-level performance, learn human–AI cooperation, and operate at internet scale.