We started off this book with a simple discussion of what artificial general intelligence (AGI) is. In short, AGI is our attempt at generalizing an intelligent system to solve multiple tasks. RL is often thought of as a stepping stool to AGI primarily because it tries to generalize state-based learning. While both RL and AGI take deep inspiration from how we think, be it rewards or possibly consciousness itself, the former tends to incorporate direct analogies. The actor-critic concept in RL is an excellent example of how we use an interpretation of human psychology to create a form of learning. For instance, we humans often consider the consequences of our actions and determine the advantages they may or may not give us. This example is perfectly analogous to actor-critic and advantage methods we use in RL. Take this further and...
Hands-On Reinforcement Learning for Games
By :
Hands-On Reinforcement Learning for Games
By:
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
Other Books You May Enjoy
Customer Reviews