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    Hands-On Reinforcement Learning for Games. Implementing self-learning agents in games using artificial intelligence techniques Micheal Lanham

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Hands-On Reinforcement Learning for Games. Implementing self-learning agents in games using artificial intelligence techniques Micheal Lanham - okladka książki

    Hands-On Reinforcement Learning for Games. Implementing self-learning agents in games using artificial intelligence techniques Micheal Lanham - okladka książki

    Hands-On Reinforcement Learning for Games. Implementing self-learning agents in games using artificial intelligence techniques Micheal Lanham - audiobook MP3

    Hands-On Reinforcement Learning for Games. Implementing self-learning agents in games using artificial intelligence techniques Micheal Lanham - audiobook CD

    Autor:
    Micheal Lanham
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    432
    Dostępne formaty:
         PDF
         ePub
         Mobi
    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.

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    O autorze książki

    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 such as 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.

    Micheal Lanham - pozostałe książki

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