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Reinforcement Learning

A type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a cumulative reward.

AbbreviationRL
Categoryai

Overview

RL is inspired by behavioral psychology. An agent interacts with an environment, receives feedback in the form of rewards or penalties, and adjusts its strategy (policy) accordingly.

Key Concepts

  • Agent: The learner or decision-maker.
  • Environment: Everything the agent interacts with.
  • Action: What the agent does.
  • State: The current situation of the agent.
  • Reward: Feedback from the environment.

Applications

  • Game playing (e.g., AlphaGo).
  • Robotics.
  • Autonomous vehicles.
  • Recommendation systems.

Related Terms

Supervised LearningUnsupervised LearningPPOReward Model