Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. Unlike supervised learning, which uses labeled data, RL relies on trial and error to maximize cumulative rewards. 🤖

Key Concepts

  • Agent: The learner or decision-maker in the system.
  • Environment: The world in which the agent operates.
  • Reward Signal: Feedback from the environment guiding the agent's learning.
  • Policy: Strategy that the agent uses to determine actions.
  • Value Function: Estimates the long-term benefit of states or actions.

Applications

  • Game playing (e.g., AlphaGo)
  • Robotics control
  • Autonomous systems
  • Resource management

Learning Resources

Reinforcement Learning Overview
Q Learning Process