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    Hands-On Game AI with Python: Implement self-learning AI agents in games using reinforcement learning algorithms and techniques

    Posted By: IrGens
    Hands-On Game AI with Python: Implement self-learning AI agents in games using reinforcement learning algorithms and techniques

    Hands-On Game AI with Python: Implement self-learning AI agents in games using reinforcement learning algorithms and techniques by Micheal Lanham
    English | February 11, 2020 | ISBN: 1839214937 | EPUB/PDF | 407 pages | 17/18.98 MB

    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 game AI 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.