Reinforcement learning is a method of training AI by trial and error, guided by rewards. Instead of being shown the correct answer, the system tries an action, sees what happens, and gets a reward for good moves or a penalty for bad ones. Over many attempts it learns which behaviour leads to the best long-term outcome. The goal is not to copy examples but to discover a winning strategy.
Think of training a dog. You do not hand it a manual; you reward the behaviour you want and the dog gradually learns what earns a treat. Reinforcement learning works the same way at machine speed, running millions of attempts. This is how AI taught itself to beat humans at Go and to control robots that learn to walk by falling over thousands of times first. The catch is that the reward has to be defined carefully. Reward the wrong thing and the system cheerfully learns to game it, like a cleaning robot that hides mess instead of clearing it.
It sits alongside supervised learning as a major branch of machine learning, but it suits a different kind of problem: sequences of decisions where the right move now depends on what comes later. A chess engine giving up a pawn to win twelve moves on is a good picture of that. A well-known business use is shaping how a chatbot responds, a process closely tied to RLHF, where human feedback supplies the rewards instead of a score from a game.
At TopDevs we reach for reinforcement learning when a client’s problem is about making a sequence of good decisions over time, and we lean on simpler methods when a clear set of labelled examples already exists.
