Hands-On Reinforcement Learning for Games
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 practical approach.
Offered by
Difficulty Level
Intermediate
Completion Time
14h24m
Language
English
About Book
Who Is This Book 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.
Hands-On Reinforcement Learning for Games
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 14h24m total length
Understanding Rewards-Based Learning
Dynamic Programming and the Bellman Equation
Monte Carlo Methods
Temporal Difference Learning
Exploring SARSA
Going Deep with DQN
Going Deeper with DDQN
Policy Gradient Methods
Optimizing for Continuous Control
All about Rainbow DQN
Exploiting ML-Agents
DRL Frameworks
3D Worlds
From DRL to AGI
Related Resources
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