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  1. Hands-on reinforcement learning for games
    implementing self-learning agents in games using artificial intelligence techniques
    Autor*in: Lanham, Micheal
    Erschienen: 2020
    Verlag:  Packt, Birmingham

    The AI revolution is here and it is embracing games. Game developers are being challenged to enlist cutting edge AI as part of their games. In this book, you will look at the journey of building capable AI using reinforcement learning algorithms and... mehr

    Zugang:
    Aggregator (lizenzpflichtig)

    Standort:
    Technische Universität Bergakademie Freiberg, Bibliothek 'Georgius Agricola'
    Fernleihe:
    keine Fernleihe

    Standort:
    Hochschulbibliothek Friedensau
    Signatur:
    Online-Ressource
    Fernleihe:
    keine Fernleihe

    Standort:
    Hochschule für Wirtschaft und Umwelt Nürtingen-Geislingen, Bibliothek Nürtingen
    Signatur:
    eBook ProQuest
    Fernleihe:
    keine Fernleihe

    Standort:
    Universität Ulm, Kommunikations- und Informationszentrum, Bibliotheksservices
    Fernleihe:
    keine Fernleihe

     

    The AI revolution is here and it is embracing games. Game developers are being challenged to enlist cutting edge AI as part of their games. In this book, you will look at the journey of building capable AI using reinforcement learning algorithms and techniques. You will learn to solve complex tasks and build next-generation games using a ...

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Ebook
    Format: Online
    ISBN: 9781839216770
    Schlagworte: Computer games-Programming; Computer games-Programming; Electronic books
    Umfang: 1 Online Ressource (v, 407 Seiten)
    Bemerkung(en):

    Description based on publisher supplied metadata and other sources
  2. Hands-on reinforcement learning for games
    implementing self-learning agents in games using artificial intelligence techniques
    Autor*in: Lanham, Micheal
    Erschienen: January 2020
    Verlag:  Packt, Birmingham ; Mumbai

    bExplore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlow/b h4Key Features/h4 ulliGet to grips with the different reinforcement and DRL algorithms for game... mehr

    Standort:
    Universität der Bundeswehr München, Universitätsbibliothek
    Fernleihe:
    uneingeschränkte Fernleihe, Kopie und Ausleihe

     

    bExplore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlow/b h4Key Features/h4 ulliGet to grips with the different reinforcement and DRL algorithms for game development /li liLearn how to implement components such as artificial agents, map and level generation, and audio generation /li liGain insights into cutting-edge RL research and understand how it is similar to artificial general research/li/ul h4Book Description/h4 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. h4What you will learn/h4 ulliUnderstand how deep learning can be integrated into an RL agent /li liExplore basic to advanced algorithms commonly used in game development /li liBuild agents that can learn and solve problems in all types of environments /li liTrain a Deep Q-Network (DQN) agent to solve the CartPole balancing problem /li liDevelop game AI agents by understanding the mechanism behind complex AI /li liIntegrate all the concepts learned into new projects or gaming agents/li/ul h4Who this book is for/h4 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

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Ebook
    Format: Online
    ISBN: 9781839216770
    RVK Klassifikation: ST 324
    Schlagworte: COMPUTERS / Intelligence (AI) &amp; Semantics; COMPUTERS / Machine Theory; Computerspiel; Bestärkendes Lernen <Künstliche Intelligenz>; Deep learning
    Umfang: 1 Online-Ressource (v, 407 Seiten)
  3. Hands-on reinforcement learning for games
    implementing self-learning agents in games using artificial intelligence techniques
    Autor*in: Lanham, Micheal
    Erschienen: [2020]; © 2020
    Verlag:  Packt, Birmingham, England

    Standort:
    Fachhochschule Dortmund, Hochschulbibliothek
    Fernleihe:
    uneingeschränkte Fernleihe, Kopie und Ausleihe
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Ebook
    Format: Online
    ISBN: 9781839216770
    Schlagworte: Computerspiel; Operante Konditionierung; Künstliche Intelligenz
    Weitere Schlagworte: Computer games ; Programming
    Umfang: 1 Online-Ressource (V, 407 Seiten), Illustrationen
  4. Hands-on reinforcement learning for games
    implementing self-learning agents in games using artificial intelligence techniques
    Autor*in: Lanham, Micheal
    Erschienen: [2020]
    Verlag:  Packt Publishing, Birmingham ; Mumbai ; ProQuest Ebook Central, [Ann Arbor]

    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... mehr

     

    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.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Hinweise zum Inhalt
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Ebook
    Format: Online
    ISBN: 9781839216770
    RVK Klassifikation: ST 324
    Schlagworte: Deep learning; Bestärkendes Lernen <Künstliche Intelligenz>; Computerspiel;
    Umfang: 1 Online-Ressource (407 Seiten), Illustrationen
  5. Hands-on reinforcement learning for games
    implementing self-learning agents in games using artificial intelligence techniques
    Autor*in: Lanham, Micheal
    Erschienen: [2020]; © 2020
    Verlag:  Packt, Birmingham, England

    Standort:
    Fachhochschule Dortmund, Hochschulbibliothek
    Fernleihe:
    keine Fernleihe
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Ebook
    Format: Online
    ISBN: 9781839216770
    Schlagworte: Computer games ; Programming
    Umfang: 1 Online-Ressource (V, 407 Seiten), Illustrationen