Episode 7: How Does AI Learn? A Gentle Introduction to Reinforcement Learning Through Trial and Error

Starting with the Basics: What Is Reinforcement Learning?

To get good at something, we usually go through a process of trial and error—making mistakes and slowly figuring out what works. Think back to when you first learned how to ride a bicycle. At first, you may have struggled to keep your balance or didn’t know how to pedal properly. But after trying again and again, you naturally picked up the sense of “this is how it works.”

This process of “trying something out and remembering what worked” is at the heart of what’s known in the AI world as “reinforcement learning.”

How Does AI Learn? The Mechanism Behind Its Decision-Making

In reinforcement learning, AI tries out different actions within a certain environment and uses the resulting “rewards” as clues to figure out which actions are most beneficial.

Here, the “environment” refers to the setting or stage where the AI operates. The “actions” are the choices it makes within that setting, and the “rewards” are like prizes it receives based on those actions. For example, in a game, earning a high score would be considered a reward. In self-driving cars, successfully reaching a destination safely could be seen as one.

In this method, AI doesn’t start off knowing the right answers. In fact, it begins from a place of uncertainty—“you won’t know until you try.” That’s why it repeatedly experiments with different actions, observing which ones succeed or fail, and gradually builds its own strategies or rules based on those outcomes.

This approach differs from supervised learning (where AI learns from correct examples) or unsupervised learning (where it looks for patterns in data without labels). We’ll explore those methods more deeply in other articles.

Where Is It Actually Used? Real-World Applications and Challenges

One well-known area where reinforcement learning shines is in board games like Go or chess. A particularly famous example is AlphaGo, developed by DeepMind (a subsidiary of Google), which made headlines when it defeated top human players. AlphaGo learned by playing countless matches against itself, gradually discovering moves that led to victory.

More recently, reinforcement learning has started being used in self-driving technology. Navigating complex environments—like responding to traffic lights or avoiding pedestrians—requires quick decision-making about what action to take at any given moment. This kind of trial-and-error-based learning helps build that decision-making ability effectively.

However, there are challenges with this approach. One major issue is that reinforcement learning needs an environment where failure doesn’t cause real harm. In many cases, letting an AI freely experiment in real-world scenarios isn’t practical or safe. That’s why carefully designed simulation environments are essential for training.

Another important challenge is helping AI apply what it has learned to new situations—a skill called “generalization.” In other words, it’s not enough for AI to perform well only under specific conditions; it must also adapt its knowledge when faced with similar but unfamiliar circumstances. We’ll talk more about this point in our next article.

Is It Similar to Human Learning? The Power of Experience

The term “reinforcement learning” might sound technical or intimidating at first glance. But at its core, it closely resembles how we humans learn every day—by reflecting on what went well and what didn’t work so well, then developing our own methods based on those experiences.

We all grow through small successes and failures in our daily lives. From that perspective, reinforcement learning can be seen as a way of giving computers their own form of “experience.” And perhaps that idea makes this technology feel just a little more relatable.

In our next article, we’ll dive into this idea of applying experience to new situations—what we call “generalization.” Let’s explore together how AI develops this kind of adaptability.