Introduction 🌟
Reinforcement Learning (RL) is a critical branch of machine learning where agents learn to make decisions by interacting with an environment. Unlike supervised learning, RL focuses on learning through trial and error, guided by rewards and penalties.
Core Concepts 🧠
- Agent-Environment Interaction: The agent takes actions and receives feedback in the form of rewards.
- Reward Signal: A scalar value indicating the immediate benefit of an action.
- Policy: A strategy that the agent employs to determine actions based on current states.
- Value Function: Estimates the long-term reward an agent can expect from a state.
- Q-Learning: A model-free algorithm that learns the value of actions in states.
Applications 🚀
- Game Playing (e.g., AlphaGo)
- Robotics
- Autonomous Vehicles
- Personalized Recommendations
Further Reading 📚