In this chapter, we looked specifically at one of the more state-of-the-art advances in DRL from DeepMind called Rainbow DQN. Rainbow combines several improvements layered on top of DQN that allow dramatic increases in training performance. As we have already covered many of these improvements, we only needed to review a couple of new advances. Before doing that though, we installed TensorBoard as a tool to investigate training performance. Then, we looked at the first advancement in distributional RL and how to model the action by understanding the sampling distribution. Continuing with distributions, we then looked at noisy network layers—network layers that don't have individual weights but rather individual distributions to describe each weight. Building on this example, we moved onto Rainbow DQN with our last example, finishing off with a quick discussion...
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.
Related Content you might be interested in
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
Other Books You May Enjoy
Customer Reviews