🚀 Reinforcement Learning Tutorial

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. Unlike supervised learning, RL doesn't rely on labeled data but instead uses reward signals to improve its strategy over time.

🧠 Core Concepts

• Agent: The learner or decision-maker.
• Environment: The world the agent interacts with.
• State: A representation of the current situation.
• Action: A move or decision the agent can take.
• Reward: Feedback from the environment to guide learning.

📊 Algorithm Categories

1. Model-Free Methods
   • Q-Learning 🔄
   • Deep Q-Networks (DQN) 🤖
2. Model-Based Methods
   • Dynamic Programming 📈
   • Monte Carlo Tree Search 🧾
3. Policy Gradient Methods
   • REINFORCE 🎯
   • Proximal Policy Optimization (PPO) 📈

🧪 Application Cases

• Game Playing (e.g., AlphaGo 🎮)
• Robotics (e.g., autonomous navigation 🤖)
• Resource Management (e.g., energy optimization 💡)

📘 Further Reading

For a deeper dive into related topics:

• Machine Learning Fundamentals
• Deep Learning Overview