Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to achieve a goal. This guide provides an overview of RL concepts, algorithms, and applications.

Key Concepts

  • Agent: The decision-making entity in an RL system.
  • Environment: The context in which the agent operates, which provides feedback and rewards based on the agent's actions.
  • State: The current situation or configuration of the environment.
  • Action: A possible move or decision that the agent can make.
  • Reward: A scalar value that indicates how good or bad an action is.

Popular RL Algorithms

  • Q-Learning: A value-based RL algorithm that learns a Q-function that estimates the best action for a given state.
  • Deep Q-Network (DQN): A combination of Q-learning and deep learning, where a neural network is used to approximate the Q-function.
  • Policy Gradient: An algorithm that directly learns a policy, which is a function that maps states to actions.
  • SARSA: A policy gradient algorithm that learns a policy by considering the current state, action, reward, and next state.

Applications of RL

  • Robotics: Control robots to perform tasks such as navigation and manipulation.
  • Game Playing: Develop AI agents that can play games like chess, Go, and poker.
  • Autonomous Vehicles: Enable self-driving cars to navigate and make decisions on the road.
  • Financial Markets: Create trading agents that can make profitable investments.

Reinforcement Learning Example

For more information on RL, check out our Introduction to Machine Learning.