Reinforcement Learning (RL) is a branch of machine learning that focuses on how intelligent agents ought to take actions in an environment to maximize some notion of cumulative reward. Here's a quick overview:

  • Basic Concepts:

    • Agent: The decision-maker in an environment.
    • Environment: The surroundings in which the agent operates.
    • State: The current situation or configuration of the environment.
    • Action: The choice made by the agent.
    • Reward: The outcome of taking an action in a state, which guides the agent towards optimal behavior.
    • Policy: The strategy or set of rules that the agent uses to select actions.

  • Types of RL:

    • Tabular RL: When the state and action spaces are discrete and finite.
    • Model-Free RL: When the environment model is unknown.
    • Model-Based RL: When the environment model is known.

  • Key Algorithms:

    • Q-Learning: A model-free, tabular RL algorithm.
    • Deep Q-Network (DQN): An extension of Q-Learning that uses deep neural networks.
    • Policy Gradient Methods: Focus on learning a policy directly, rather than a value function.

  • Applications:

    • Robotics: Teaching robots to navigate or manipulate objects.
    • Games: Playing games like chess, Go, or video games.
    • Finance: Algorithmic trading.
    • Autonomous Vehicles: Making decisions in complex traffic scenarios.

For more information on RL and its applications, check out our Advanced RL Techniques guide.

Reinforcement Learning Diagram