Extending from where we left off with DQN, we looked at ways of extending this model with CNN and adding additional networks to create double DQN and dueling DQN, or DDQN. Before exploring CNN, we looked at what visual observation encoding is and why we need it. Then, we briefly introduced CNN and used the TensorSpace Playground to explore some well-known, state-of-the-art models. Next, we added CNN to a DQN model and used that to play the Atari game environment Pong. After, we took a closer look at how we could extend DQN by adding another network as the target and adding another network to duel against or to contradict the other network, also known as the dueling DQN or DDQN. This introduced the concept of advantage in choosing an action. Finally, we looked at extending the experience replay buffer so that we can prioritize events that get captured there. Using this...
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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