Reinforcement Learning

A type of machine learning where a system learns to make decisions by performing actions in an environment and receiving rewards or penalties based on the outcomes. Unlike supervised learning, which learns from labelled examples, reinforcement learning discovers optimal strategies through trial and error. In accessibility applications, reinforcement learning enables adaptive systems that personalize content and difficulty to individual user needs—for example, adjusting the challenge level of cognitive training games for users with ADHD based on their real-time performance, or optimizing assistive technology interfaces to match a user's evolving abilities.