Reinforcement Learning Tutorial 🤖

Reinforcement Learning (RL) is a branch of machine learning where an agent learns to make decisions by interacting with an environment. Unlike supervised learning, RL focuses on learning from rewards and punishments to optimize long-term outcomes.

Key Concepts 📘

• Agent: The learner or decision-maker.
• Environment: The world the agent interacts with.
• Reward Signal: Feedback from the environment guiding the agent's actions.
• Policy: Strategy that the agent uses to choose actions.
• Value Function: Measures the long-term reward an agent can expect.
• Model: Representation of the environment (optional).

Applications 🚀

• Autonomous Driving 🚗
• Game Playing 🎮 (e.g., AlphaGo)
• Robotics 🤖
• Recommendation Systems 📈
• Resource Management 💡

Learning Resources 🧠

• Introduction to RL
• Q-Learning Algorithm
• Deep RL with Neural Networks

For hands-on practice, explore our interactive RL simulator to visualize agent-environment interactions. 📊