Discover the model-free algorithm that learns the quality of actions, guiding agents on what to do in different circumstances.

Understand how reinforcement learning objectives are defined through immediate rewards for actions in specific states.

Explore how agents define their behavior by mapping states to actions, representing their strategy for achieving goals.

Learn about the external system with which reinforcement learning agents interact, defining rules, dynamics, and constraints.

Understand the entity that autonomously plans and executes a series of actions in pursuit of a goal, with the ability to adapt to changes in its environment.

Discover how AI agents learn to make decisions through trial and error to maximize cumulative rewards.

Explore the technology that converts images of text into machine-readable format, from scanned documents to photos.

Learn how AI partitions images into meaningful regions by assigning each pixel to a specific category or object.