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. It is a subset of machine learning that focuses on how software agents ought to take actions in an environment to maximize some notion of cumulative reward.

Key Concepts

  • Agent: The decision-making entity that learns from the environment.
  • Environment: The system with which the agent interacts.
  • State: The current situation or context in which the agent operates.
  • Action: The decision made by the agent.
  • Reward: The feedback given to the agent after taking an action.

Types of Reinforcement Learning

  • Tabular RL: The agent learns from a table of known states and actions.
  • Model-based RL: The agent builds a model of the environment and uses it to make decisions.
  • Model-free RL: The agent learns directly from interactions with the environment without building a model.

Challenges

  • Exploration vs. Exploitation: Balancing the need to explore new actions to learn more about the environment with the need to exploit known actions that are known to be effective.
  • Credit Assignment: Determining which actions led to a particular outcome.

Applications

  • Robotics: Teaching robots to perform tasks like walking or manipulating objects.
  • Games: Developing AI for playing games like chess, Go, or video games.
  • Autonomous Vehicles: Helping cars learn to drive safely and efficiently.
  • Healthcare: Personalizing treatment plans for patients.

Reinforcement Learning Diagram

For more information on Reinforcement Learning, check out our Reinforcement Learning Tutorial.