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Game Lab: Unity Simulation

Welcome to the Unity Game Development section of Ants Saga! Here you'll learn how to create the simulation environment that will serve as the foundation for your Reinforcement Learning experiments.

🎮 What You'll Build​

The Proto Lab​

The Proto Lab is the Unity simulation environment where your RL agents will learn and interact. It's designed to be:

  • Modular: Easy to modify and extend
  • Realistic: Physics-based simulation
  • Scalable: Can handle multiple agents
  • Configurable: Parameters can be adjusted for different experiments

🛠️ Development Workflow​

1. Environment Setup​

  • Unity 2022.3 LTS installation
  • Project structure and organization
  • Essential packages and assets

2. Core Systems​

  • Agent movement and physics
  • Environment interactions
  • Reward system implementation
  • State representation

3. Integration​

  • Python-Unity communication
  • mlvlab integration
  • Real-time training setup

🎯 Learning Objectives​

By the end of this section, you'll be able to:

  • ✅ Set up a Unity project for RL simulation
  • ✅ Create interactive environments
  • ✅ Implement reward systems
  • ✅ Connect Unity with Python for training
  • ✅ Debug and optimize simulations

🔗 Next Steps​

Once you've mastered Unity development:

  1. Move to Q-Learning Python - Implement your first RL algorithm
  2. Explore Getting Started - If you need help with setup
  3. Check Glossary - For any unfamiliar terms

Ready to start building? Let's dive into the first lesson!