In plain words
Reinforcement learning is a branch of machine learning in which an agent learns what actions to take by interacting with an environment and receiving feedback, often called rewards.
A closer look
At each step, an agent observes a situation, chooses an action, and receives a result: a new situation and a reward signal. The central challenge is credit assignment: working out which earlier choices helped or hurt a later outcome. A choice that has a small immediate cost can still be valuable if it improves the long-term result.
An agent also has to balance exploration and exploitation. Exploration tries actions that may reveal better strategies; exploitation uses what the system already believes works. The reward function matters enormously, because it defines what the learner is pushed to optimize. Poorly designed rewards can encourage shortcuts that score well without achieving the intended goal.
In practice
A game-playing agent receives a positive reward for winning and a negative reward for losing. After many games, it adjusts its strategy to make winning outcomes more likely, even though it was never given a list of correct moves for every position.
A useful distinction
Reinforcement learning does not mean a system learns exactly what its designers want. It learns to optimize the reward signal it receives, which can differ from the real-world goal. It is also distinct from reinforcement learning from human feedback (RLHF), which uses human preferences as part of the feedback process.