An agent and an environment exchange information in a repeating cycle. The agent observes the current situation, chooses an action, and sends that action to the environment. The environment changes and returns a reward together with the next observation. The agent then uses that reward to adjust how it will choose actions in the future, and the cycle begins again from the new observation.
Each element has a distinct job. The observation is the agent's only window on the world; whatever it cannot observe, it cannot use. The action is the agent's only way to influence what happens next. The reward is a single number scoring the immediate consequence of the action just taken. The update is where learning actually occurs: without it, the agent would repeat the same behavior forever and nothing would improve.
The learning signal enters at the reward and is consumed by the update. A reward arriving after an action tells the agent that the outcome was good or bad, but it arrives attached to the whole situation, not to a specific decision. That ambiguity is the central difficulty of reinforcement learning and the reason the update step must reason about credit rather than simply copy an answer.