Reinforcement Learning (RL) is a branch of machine learning where agents learn to make decisions by interacting with an environment. Unlike supervised learning, RL focuses on learning from rewards and feedback to optimize long-term outcomes.
Key Concepts 📚
- Agent-Environment Interaction: The core of RL involves an agent taking actions in an environment to maximize cumulative rewards.
- Reward Signal: A feedback mechanism that guides the agent toward desirable behavior.
- Policy: A strategy that dictates which actions the agent should take in given states.
- Value Function: Estimates the long-term reward an agent can expect from a state or action.
Applications 🚀
- Game Playing: RL is used in training AI for games like chess, Go, and video games.
- Robotics: Enables robots to learn complex tasks through trial and error.
- Autonomous Vehicles: Helps vehicles make real-time decisions in dynamic environments.
- Resource Management: Optimizes systems like energy grids or network traffic.
Learning Resources 🌐
- Explore our RL Course for hands-on projects and tutorials.
- Dive deeper into Q-Learning or Deep RL with our Advanced Topics Guide.