Up until now, we have considered just the extrinsic or external rewards an agent may receive in an environment. The Hallway example, for instance, gives a +1 external reward when the agent reaches the goal, and a -1 external reward if it gets the wrong goal. However, real animals like us can actually learn based on internal motivations, or by using an internal reward function. A great example of this is a baby (a cat, a human, or whatever) that has an obvious natural motivation to be curious through play. The curiosity of playing provides the baby with an internal or intrinsic reward, but the actual act itself gives it a negative external or extrinsic reward. After all, the baby is expending energy, a negative external reward, yet it plays on and on in order to learn more general information about its environment. This, in turn, allows it to explore more of...
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.
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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