An agent lives in an environment. At every time step it observes a state s, picks an action a, then receives a reward r and a next state s'.

Its goal is to learn a policy — a way to choose actions — that maximizes the long-run sum of rewards.

Below is a tiny world. The agent starts on the bottom-left and must reach the goal (green) on the bottom-right. The red strip is a cliff: stepping on it costs -100 and sends the agent back to start. Every other step costs -1.