What is Reinforcement Learning?

Reinforcement Learning, or RL, is a subset of Machine Learning, but it has an important difference from common machine learning methods. In machine learning, we usually work with a dataset. This means we train a model, patterns are learned, and then it makes predictions on new data, for example a dog and cat classification model.

But in Reinforcement Learning, everything changes because we do not have data that is simply learned from. Instead, an agent enters an environment, decides which action to take, and then observes the result of its decision. Based on the reward it receives, it learns which behavior is better.

Difference Between RL and Other Machine Learning Methods:

In Supervised Learning or Unsupervised Learning, we usually have: • A fixed dataset • Data collected in advance • The model only trains on that data

But in RL: • The Agent explores the environment itself • Makes decisions • Observes the results • And learns from its own experiences

This means learning is completely dependent on interaction, experience, and feedback from the environment.

Main Components of Reinforcement Learning

In RL, we usually have three main components:

1. Agent
2. Environment
3. Reward

The Agent learning cycle in RL works as follows:

First, the agent observes the current state of the environment. Based on this observation, it selects an action and executes it. As a result of this action, the environment transitions into a new state and provides a reward to the agent. Finally, the agent uses this feedback to learn and improve its future decisions.

This cycle is repeated many times until the Agent can make better decisions.

What Problems Was RL Designed For?

The biggest difference between RL and many ML methods is that decisions are dependent on each other. A decision does not only affect the current moment but also affects the future. For example, in a content recommendation system:

• If I show this video, what happens?

• What will the user watch next?

• Will the user stay longer on the platform?

• Will engagement increase?

Here, we no longer have a simple prediction problem. We are dealing with a chain of decisions.

That is why RL is widely used in areas such as:

• Robotics • Games • Recommendation systems • Advertising • Long-term decision-making

Difference Between Rule-Based Systems and RL:

In many systems, decisions can be made using simple rules.

For example: But when the number of rules increases, the system becomes complex and uncontrollable.

This is where RL comes in. Instead of manually defining all decisions, the Agent learns what the best strategy is.

For example: • What happens if I give a 10% discount? • What if the user does not buy without a discount? • If I give a 50% discount, will the profit disappear?

The Agent experiences these situations and gradually learns which decision leads to the best long-term outcome.

Do Instagram and YouTube Use RL?

Systems like YouTube and Instagram are not purely based on RL and are instead a combination of several Machine Learning approaches. However, many RL ideas can be seen in them.

Because these systems constantly need to decide:

• Which post should be shown?

• How content should be ordered?

• What should be shown after this video?

Each decision can influence the user’s future behavior. However, full RL at the scale of billions of users is extremely complex and expensive, so these systems usually rely on a combination of different ML methods.

One of the Most Important Challenges in RL:

In RL, the result of a decision is not always immediately known. An agent may take an action now, but the actual reward may appear several steps later. For example, in games:

• The current move may have no immediate reward • But several moves later it may lead to a win

This raises an important question: which decision actually caused the good outcome? This is called the Credit Assignment Problem.

Another important idea in RL is that the agent should not only rely on immediate rewards but also estimate the future. This concept is called Temporal Difference (TD). TD suggests that if a state is good, the next state is also likely to be valuable. Therefore, by combining current value and future estimation, the agent can learn which path leads to higher rewards.

Another major challenge in RL is whether the agent should exploit what it already knows or explore new options. In other words, should it always behave greedily or not?

This is one of the most important challenges in RL, and different methods exist to solve it, but future articles will discuss RL challenges and algorithms in more detail.

Conclusion

Reinforcement Learning is a different framework in machine learning in which the agent, instead of learning from a fixed dataset, learns through direct interaction with the environment. In this method, data is not available beforehand; instead, it is generated over time through experience. The agent makes decisions, observes the results, receives rewards, and gradually improves its behavior. Unlike many traditional machine learning methods that focus on data-centric training, RL learning is completely dependent on experience, time, and environmental feedback. Ultimately, the goal is for the agent to make decisions over time that generate the highest reward.

Used from this Article: Reinforcement Learning: A Survey (1996)