Think about how you teach a dog to sit. You say “sit,” and if the dog sits, you give it a treat. If it doesn't, it gets nothing. Over time, the dog connects the action of sitting with the reward of a treat and starts to sit on command. This is learning from trial and error, based on feedback. In the world of artificial intelligence, this process has a name: reinforcement learning.

Unlike other types of machine learning where a model learns from a labeled dataset, an RL agent learns on its own. It's like a baby learning to walk. It tries different movements, falls down a few times (negative feedback), and eventually figures out the right sequence of muscle movements to stay upright and move forward (positive feedback). The core of this process is the interaction between the agent and its environment.

In RL, we have two main characters: the agent and the environment.

• The agent is the learner or decision-maker. It could be a robot learning to navigate a room, a program learning to play chess, or an algorithm learning to manage investments.
• The environment is the world the agent interacts with. It’s everything outside the agent. For the robot, it's the room; for the chess program, it's the chessboard and the opponent.

The agent and environment are locked in a continuous loop of interaction. It’s a simple, repeating cycle.

Let's break down the steps in this loop:

1. The agent observes the current state of the environment.
2. Based on this state, the agent chooses an action.
3. The environment reacts to the action, transitioning to a new state and providing a reward to the agent.
4. The agent receives this new state and reward, and the loop begins again.

These three components, state, action, and reward, are the fundamental building blocks of reinforcement learning.

The agent’s goal isn't just to get the biggest immediate reward. A mouse in a maze might see a small piece of cheese one turn to the left, but the big block of cheese is ten turns to the right. If it only focuses on the immediate reward, it will always go for the small piece and miss out on the larger prize.

Instead, the agent's objective is to maximize its cumulative reward over the long run. It needs to learn a strategy, called a policy, that chooses actions leading to the greatest total reward over time. Sometimes this means sacrificing a small, short-term reward for a much larger one later.

This diagram shows a simple environment. The agent moves from state to state. The goal is to find a path that ends in one of the red circles, which give the highest rewards. The agent learns that some paths are better than others by exploring and seeing which sequences of actions lead to the biggest payoffs.

This core loop of state, action, and reward is the foundation of all reinforcement learning. By repeatedly interacting with its environment and learning from the feedback, an agent can master complex tasks, from playing games to controlling robotic arms.