Learning by trying things, getting rewards, and doing more of what earns them.
In reinforcement learning, nobody gives the AI the right answer. Instead, it gets a score, called a reward, for the results of what it does. It tries different moves, then does more of what earned rewards and less of what cost it, gradually building a strategy.
It works especially well where winning and losing are clear, like games. AlphaGo, which beat Lee Sedol at Go in 2016, used reinforcement learning. It's now used to polish large language models too: RLHF rewards the answers people prefer, and reasoning models are trained on tasks with checkable answers, such as math problems.
Think of it like this
Imagine starting a new game with no tutorial. You press buttons, remember the moves that raise your score, and avoid the ones that lose a life. Reinforcement learning does that over millions of rounds.
For example
In 2013 researchers showed an AI that learned to score in old Atari games from the screen pixels alone.
Records about this
Source-checked moments from AI history.
The first frontier LLM to appear in a journal after formal peer review. A crack in the practice of relying on corporate technical reports
An event that changed public perception of what AI can do. It became the backdrop for later discussions of investment and policy
An early case showing that deep reinforcement learning works from screen input alone