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Learn Unity ML-Agents：Fundamentals of Unity Machine Learning
ThisbookisintendedfordeveloperswithaninterestinusingMachinelearningalgorithmstodevelopbettergamesandsimulationswithUnity.ThereaderwillberequiredtohaveaworkingknowledgeofC#andabasicunderstandingofPython.
目錄(116章)
倒序
- 封面
- Title Page
- Copyright and Credits
- Learn Unity ML - Agents - Fundamentals of Unity Machine Learning
- Dedication
- Packt Upsell
- Why subscribe?
- PacktPub.com
- Contributors
- About the author
- About the reviewers
- Packt is searching for authors like you
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Download the color images
- Conventions used
- Get in touch
- Reviews
- Introducing Machine Learning and ML-Agents
- Machine Learning
- Training models
- A Machine Learning example
- ML uses in gaming
- ML-Agents
- Running a sample
- Setting the agent Brain
- Creating an environment
- Renaming the scripts
- Academy Agent and Brain
- Setting up the Academy
- Setting up the Agent
- Setting up the Brain
- Exercises
- Summary
- The Bandit and Reinforcement Learning
- Reinforcement Learning
- Configuring the Agent
- Contextual bandits and state
- Building the contextual bandits
- Creating the ContextualDecision script
- Updating the Agent
- Exploration and exploitation
- Making decisions with SimpleDecision
- MDP and the Bellman equation
- Q-Learning and connected agents
- Looking at the Q-Learning ConnectedDecision script
- Exercises
- Summary
- Deep Reinforcement Learning with Python
- Installing Python and tools
- Installation
- Mac/Linux installation
- Windows installation
- Docker installation
- GPU installation
- Testing the install
- ML-Agents external brains
- Running the environment
- Neural network foundations
- But what does it do?
- Deep Q-learning
- Building the deep network
- Training the model
- Exploring the tensor
- Proximal policy optimization
- Implementing PPO
- Understanding training statistics with TensorBoard
- Exercises
- Summary
- Going Deeper with Deep Learning
- Agent training problems
- When training goes wrong
- Fixing sparse rewards
- Fixing the observation of state
- Convolutional neural networks
- Experience replay
- Building on experience
- Partial observability memory and recurrent networks
- Partial observability
- Memory and recurrent networks
- Asynchronous actor – critic training
- Multiple asynchronous agent training
- Exercises
- Summary
- Playing the Game
- Multi-agent environments
- Adversarial self-play
- Using internal brains
- Using trained brains internally
- Decisions and On-Demand Decision Making
- The Bouncing Banana
- Imitation learning
- Setting up a cloning behavior trainer
- Curriculum Learning
- Exercises
- Summary
- Terrarium Revisited – A Multi-Agent Ecosystem
- What was/is Terrarium?
- Building the Agent ecosystem
- Importing Unity assets
- Building the environment
- Basic Terrarium – Plants and Herbivores
- Herbivores to the rescue
- Building the herbivore
- Training the herbivore
- Carnivore: the hunter
- Building the carnivore
- Training the carnivore
- Next steps
- Exercises
- Summary
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時(shí)間：2021-08-13 15:58:44
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