The author of Rainbow: Combining Improvements in Deep Reinforcement Learning, Matteo Hessel (https://arxiv.org/search/cs?searchtype=author&query=Hessel%2C+M), did several comparisons against other state-of-the-art models in DRL, many of which we have already looked at. They performed these comparisons against the standard 2D classic Atari games with impressive results. Rainbow DQN outperformed all of the current state-of-the-art algorithms. In the paper, they used the familiar classic Atari environment. This is fine since DeepMind has a lot of data for that environment that is specific to applicable models to compare with. However, many have observed that the paper lacks a comparison between PG methods, such as PPO. Of course, PPO is an OpenAI advancement and it may have been perceived by Google DeepMind to be an infringement or just wanting to avoid...
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.
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Current Title:
Hands-On Reinforcement Learning for Games
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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