Practical Simulations for Machine Learning

Spis treści książki

• Preface
  • Resources Used in This Book
  • Audience and Approach
  • Organization of This Book
  • Using This Book
    • Our Tasks
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• I. The Basics of Simulation and Synthesis
• 1. Introducing Synthesis and Simulation
  • A Whole New World of ML
  • The Domains
    • Simulation
    • Synthesis
  • The Tools
    • Unity
    • PyTorch via Unity ML-Agents
    • Unity ML-Agents Toolkit
    • Unity Perception
  • The Techniques
    • Reinforcement Learning
    • Imitation Learning
    • Hybrid Learning
    • Summary of Techniques
  • Projects
    • Simulation Projects
    • Synthesis Projects
  • Summary and Next Steps
• 2. Creating Your First Simulation
  • Everybody Remembers Their First Simulation
  • Our Simulation
  • Setting Up
  • Creating the Unity Project
  • Packages All the Way Down
  • The Environment
    • The Floor
    • The Target
  • The Agent
    • Starting and Stopping the Agent
    • Letting the Agent Observe the Environment
    • Letting the Agent Take Actions in the Environment
    • Giving the Agent Rewards for Its Behavior
    • Finishing Touches for the Agent
    • Providing a Manual Control System for the Agent
  • Training with the Simulation
    • Monitoring the Training with TensorBoard
    • When the Training Is Complete
  • Whats It All Mean?
  • Coming Up Next
• 3. Creating Your First Synthesized Data
  • Unity Perception
  • The Process
    • Using Unity Perception
  • Creating the Unity Project
  • Creating a Scene
    • Getting the Dice Models
    • A Very Simple Scene
  • Preparing for Synthesis
    • Testing the Scenario
    • Setting Up Our Labels
    • Checking the Labels
  • Whats Next?
• II. Simulating Worlds for Fun and Profit
• 4. Creating a More Advanced Simulation
  • Setting Up the Block Pusher
  • Creating the Unity Project
  • The Environment
    • The Floor
    • The Walls
    • The Block
    • The Goal
    • The Agent
    • The Environment
  • Training and Testing
• 5. Creating a Self-Driving Car
  • Creating the Environment
    • The Track
    • The Car
    • Setting Up for ML
  • Training the Simulation
    • Training
    • When the Training Is Complete
• 6. Introducing Imitation Learning
  • Simulation Environment
    • Creating the Ground
    • Creating the Goal
    • The Names Ball, Agent Ball
    • The Camera
  • Building the Simulation
    • Agent Components
    • Adding Heuristic Controls
    • Observations and Goals
  • Generating Data and Training
    • Creating Training Data
    • Configuring for Training
    • Begin Training
    • Running with Our Trained Model
  • Understanding and Using Imitation Learning
• 7. Advanced Imitation Learning
  • Meet GAIL
  • Do What I Say and Do
    • A GAIL Scenario
    • Modifying the Agents Actions
    • Modifying the Observations
    • Resetting the Agent
    • Updating the Agent Properties
    • Demonstration Time
  • Training with GAIL
  • Running It and Beyond
• 8. Introducing Curriculum Learning
  • Curriculum Learning in ML
  • A Curriculum Learning Scenario
  • Building in Unity
    • Creating the Ground
    • Creating the Target
    • The Agent
  • Building the Simulation
    • Making the Agent an Agent
    • Actions
    • Observations
    • Heuristic Controls for Humans
  • Creating the Curriculum
    • Resetting the Environment
    • Curriculum Config
  • Training
  • Running It
  • Curriculum Versus Other Approaches
  • Whats Next?
• 9. Cooperative Learning
  • A Simulation for Cooperation
    • Building the Environment in Unity
      • Assembling the floor and walls
      • Adding the goal
      • Blocks of differing sizes
      • The agents
    • Coding the Agents
    • Coding the Environment Manager
    • Coding the Blocks
    • Finalizing the Environment and Agents
  • Training for Cooperation
  • Cooperative Agents or One Big Agent
• 10. Using Cameras in Simulations
  • Observations and Camera Sensors
  • Building a Camera-Only Agent
    • Coding the Camera-Only Agent
    • Adding a New Camera for the Agent
    • Seeing What the Agents Camera Sees
  • Training the Camera-Based Agent
  • Cameras and You
• 11. Working with Python
  • Python All the Way Down
  • Experimenting with an Environment
  • What Can Be Done with Python?
    • Using Your Own Environment
    • Completely Custom Training
  • Whats the Point of Python?
• 12. Under the Hood and Beyond
  • Hyperparameters (and Just Parameters)
    • Parameters
    • Reward Parameters
    • Hyperparameters
  • Algorithms
  • Unity Inference Engine and Integrations
    • Using the ML-Agents Gym Wrapper
      • Unity environments and OpenAI Baselines
    • Side Channels
      • Engine configuration channel
      • Environment parameters channel
• III. Synthetic Data, Real Results
• 13. Creating More Advanced Synthesized Data
  • Adding Random Elements to the Scene
    • Randomizing the Floor Color
    • Randomizing the Camera Position
  • Whats Next?
• 14. Synthetic Shopping
  • Creating the Unity Environment
  • A Perception Camera
    • Testing the labeler
    • Adding randomizers
  • Faking It Until You Make It
  • Using Synthesized Data
• Index

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