AI Lab: Q-Learning Agent

Welcome to the Q-Learning Python section! Here you'll implement your first Reinforcement Learning algorithm and train an agent to solve the Ants Saga challenge.

📄️ Lesson 1: Python Environment

Set up your Python environment for Q-Learning implementation

📄️ Lesson 2: Q-Learning Agent

Implement the Q-Learning algorithm from scratch

🧠 What is Q-Learning?

Q-Learning is a model-free reinforcement learning algorithm that learns the quality of actions, telling an agent what action to take under what circumstances. It's perfect for:

• Discrete action spaces
• Model-free environments
• Learning optimal policies
• Understanding RL fundamentals

🎯 The Ants Saga Challenge

In this lab, you'll train an agent to:

• Navigate through a maze-like environment
• Collect food while avoiding obstacles
• Learn from trial and error
• Optimize its path over time

🛠️ Implementation Workflow

1. Environment Setup

• Python environment configuration
• Required libraries installation
• Unity-Python communication setup

2. Q-Learning Algorithm

• Q-table initialization
• Action selection (ε-greedy)
• Q-value updates
• Policy extraction

3. Training Process

• Episode management
• Reward optimization
• Performance monitoring
• Hyperparameter tuning

4. Evaluation

• Testing trained agents
• Performance metrics
• Visualization of results

🎯 Learning Objectives

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

• ✅ Implement Q-Learning from scratch
• ✅ Train agents in Unity environments
• ✅ Debug and optimize training
• ✅ Evaluate agent performance
• ✅ Understand RL fundamentals

🔗 Prerequisites

Before starting, make sure you have:

• Unity Environment: Complete the GameDev Unity section
• Python Setup: Follow the Getting Started guide
• Basic RL Knowledge: Check the Glossary for concepts

🚀 Next Steps

Ready to start? Let's begin with setting up your Python environment!

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Let's build your first intelligent agent!