Learn Unity ML-Agents - Fundamentals of Unity Machine Learning [1 ed.] 1789138132, 9781789138139
Unity Machine Learning agents allow researchers and developers to create games and simulations using the Unity Editor, w
1,014 59 4MB
English Pages 204 Year 2018
Table of contents :
1: Introducing Machine Learning and ML-Agents
Machine Learning
ML-Agents
Running a sample
Creating an environment
Academy, Agent, and Brain
Summary
2: The Bandit and Reinforcement Learning
Reinforcement Learning
Contextual bandits and state
Exploration and exploitation
MDP and the Bellman equation
Q-Learning and connected agents
Exercises
Summary
3: Deep Reinforcement Learning with Python
Installing Python and tools
ML-Agents external brains
Neural network foundations
Deep Q-learning
Proximal policy optimization
Exercises
Summary
4: Going Deeper with Deep Learning
Agent training problems
Convolutional neural networks
Experience replay
Partial observability, memory, and recurrent networks
Asynchronous actor – critic training
Exercises
Summary
5: Playing the Game
Multi-agent environments
Adversarial self-play
Decisions and On-Demand Decision Making
Imitation learning
Curriculum Learning
Exercises
Summary
6: Terrarium Revisited – A Multi-Agent Ecosystem
What was/is Terrarium?
Building the Agent ecosystem
Basic Terrarium – Plants and Herbivores
Carnivore: the hunter
Next steps
Exercises
Summary
1: Introducing Machine Learning and ML-Agents
Machine Learning
ML-Agents
Running a sample
Creating an environment
Academy, Agent, and Brain
Summary
2: The Bandit and Reinforcement Learning
Reinforcement Learning
Contextual bandits and state
Exploration and exploitation
MDP and the Bellman equation
Q-Learning and connected agents
Exercises
Summary
3: Deep Reinforcement Learning with Python
Installing Python and tools
ML-Agents external brains
Neural network foundations
Deep Q-learning
Proximal policy optimization
Exercises
Summary
4: Going Deeper with Deep Learning
Agent training problems
Convolutional neural networks
Experience replay
Partial observability, memory, and recurrent networks
Asynchronous actor – critic training
Exercises
Summary
5: Playing the Game
Multi-agent environments
Adversarial self-play
Decisions and On-Demand Decision Making
Imitation learning
Curriculum Learning
Exercises
Summary
6: Terrarium Revisited – A Multi-Agent Ecosystem
What was/is Terrarium?
Building the Agent ecosystem
Basic Terrarium – Plants and Herbivores
Carnivore: the hunter
Next steps
Exercises
Summary
![Learn Unity ML-Agents - Fundamentals of Unity Machine Learning [1 ed.]
1789138132, 9781789138139](https://ebin.pub/img/200x200/learn-unity-ml-agents-fundamentals-of-unity-machine-learning-1nbsped-1789138132-9781789138139.jpg)
![Deep Reinforcement Learning in Unity: With Unity ML Toolkit [1 ed.]
1484265025, 9781484265024](https://ebin.pub/img/200x200/deep-reinforcement-learning-in-unity-with-unity-ml-toolkit-1nbsped-1484265025-9781484265024.jpg)
![Deep Reinforcement Learning in Unity: With Unity ML Toolkit [1 ed.]
1484265025, 9781484265024](https://ebin.pub/img/200x200/deep-reinforcement-learning-in-unity-with-unity-ml-toolkit-1nbsped-1484265025-9781484265024-o-5758905.jpg)
![Cisco unity fundamentals [2nd ed.]
1587050986, 9781587050985](https://ebin.pub/img/200x200/cisco-unity-fundamentals-2ndnbsped-1587050986-9781587050985.jpg)
![Introduction to Unity [1]](https://ebin.pub/img/200x200/introduction-to-unity-1.jpg)
