So, why are 3D worlds so important, or are at least believed to be so? Well, it all has to come down to state interpretation, or what we in DRL like to call state representation. A lot of work is being done on better representation of state for RL and other problems. The theory is that being able to represent just key or converged points of state allow us to simplify the problem dramatically. We have looked at doing just that using various techniques over several chapters. Recall how we discretized the state representation of a continuous observation space into a discrete space using a grid mesh. This technique is how we solved more difficult continuous space problems with the tools we had at the time. Over the course of several chapters since then, we saw how we could input that continuous space directly into our deep learning network. That included the...
Hands-On Reinforcement Learning for Games
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Hands-On Reinforcement Learning for Games
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Overview of this book
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.
Table of Contents (19 chapters)
Preface
Section 1: Exploring the Environment
Free Chapter
Understanding Rewards-Based Learning
Dynamic Programming and the Bellman Equation
Monte Carlo Methods
Temporal Difference Learning
Exploring SARSA
Section 2: Exploiting the Knowledge
Going Deep with DQN
Going Deeper with DDQN
Policy Gradient Methods
Optimizing for Continuous Control
All about Rainbow DQN
Exploiting ML-Agents
DRL Frameworks
Section 3: Reward Yourself
3D Worlds
From DRL to AGI
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