In this chapter, we took a very close look at how the agents in ML-Agents perceive their environment and process input. An agent's perception of the environment is completely in control by the developer, and it is often a fine balance of how much or how little input/state you want to give an agent. We played with many examples in this chapter and started by taking an in-depth look at the Hallway sample and how an agent uses rays to perceive objects in the environment. Then, we looked at how an agent can use visual observations, not unlike us humans, as input or state that it may learn from. From this, we delved into the CNN architecture that ML-Agents uses to encode the visual observations it provides to the agent. We then learned how to modify this architecture by adding or removing convolution or pooling layers. Finally, we looked at the role of memory, or how recurrent...
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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