In this chapter, we dug in and learned more of the inner workings of RL by understanding the differences between model-based versus off-model and/or policy-based algorithms. As we learned, Unity ML-Agents uses the PPO algorithm, a powerful and flexible policy learning model that works exceptionally well when training control, or what is sometimes referred to as marathon RL. After learning more basics, we jumped into other RL improvements in the form of Actor-Critic, or advantage training, and what options ML-Agents supports. Next, we looked at the evolution of PPO and its predecessor, the TRPO algorithm, how they work at a basic level, and how they affect training. This is where we learned how to modify one of the control samples to create a new joint on the Reacher arm. We finished the chapter by looking at how multi-agent policy training can be improved on, again by...
Hands-On Deep Learning for Games
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Hands-On Deep Learning for Games
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Overview of this book
The number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development.
We will take a look at the foundations of multi-layer perceptron’s to using convolutional and recurrent networks. In applications from GANs that create music or textures to self-driving cars and chatbots. Then we introduce deep reinforcement learning through the multi-armed bandit problem and other OpenAI Gym environments.
As we progress through the book we will gain insights about DRL techniques such as Motivated Reinforcement Learning with Curiosity and Curriculum Learning. We also take a closer look at deep reinforcement learning and in particular the Unity ML-Agents toolkit. By the end of the book, we will look at how to apply DRL and the ML-Agents toolkit to enhance, test and automate your games or simulations. Finally, we will cover your possible next steps and possible areas for future learning.
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Current Title:
Hands-On Deep Learning for Games
Table of Contents (18 chapters)
Preface
Deep Learning for Games
Convolutional and Recurrent Networks
GAN for Games
Building a Deep Learning Gaming Chatbot
Section 2: Deep Reinforcement Learning
Introducing DRL
Unity ML-Agents
Agent and the Environment
Understanding PPO
Rewards and Reinforcement Learning
Imitation and Transfer Learning
Building Multi-Agent Environments
Section 3: Building Games
Debugging/Testing a Game with DRL
Obstacle Tower Challenge and Beyond
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