RL is a sub-field of machine learning where the learning is carried out by a trial-and-error approach. This differs from other machine learning strategies, such as the following:

• Supervised learning: Where the goal is to learn to fit a model distribution that captures a given labeled data distribution
• Unsupervised learning: Where the goal is to find inherent patterns in a given dataset, such as clustering

RL is a powerful learning approach, since you do not require labeled data, provided, of course, that you can master the learning-by-exploration approach used in RL.

While RL has been around for over three decades, the field has gained a new resurgence in recent years with the successful demonstration of the use of deep learning in RL to solve real-world tasks, wherein deep neural networks are used to make decisions. The coupling of RL with deep learning is typically referred to as deep RL, and is the main topic of discussion of this book.

Deep RL has been successfully applied by researchers to play video games, to drive cars autonomously, for industrial robots to pick up objects, for traders to make portfolio bets, by healthcare practitioners, and copious other examples. Recently, Google DeepMind built AlphaGo, a RL-based system that was able to play the game Go, and beat the champions of the game easily. OpenAI built another system to beat humans in the Dota video game. These examples demonstrate the real-world applications of RL. It is widely believed that this field has a very promising future, since you can train neural networks to make predictions without providing labeled data.

Now, let's delve into the formulation of the RL problem. We will compare how RL is similar in spirit to a child learning to walk.

The basic problem that is solved is training a model to make predictions of some pre-defined task without any labeled data. This is accomplished by a trial-and-error approach, akin to a baby learning to walk for the first time. A baby, curious to explore the world around them, first crawls out of their crib not knowing where to go nor what to do. Initially, they take small steps, make mistakes, keep falling on the floor, and cry. But, after many such episodes, they start to stand on their feet on their own, much to the delight of their parents. Then, with a giant leap of faith, they start to take slightly longer steps, slowly and cautiously. They still make mistakes, albeit fewer than before.

After many more such tries—and failures—they gain more confidence that enables them to take even longer steps. With time, these steps get much longer and faster, until eventually, they start to run. And that's how they grow up into a child. Was any labeled data provided to them that they used to learn to walk? No. they learned by trial and error, making mistakes along the way, learning from them, and getting better at it with infinitesimal gains made for every attempt. This is how RL works, learning by trial and error.

Building on the preceding example, here is another situation. Suppose you need to train a robot using trial and error, this is how to do it. Let the robot wander randomly in the environment initially. The good and bad actions are collected and a reward function is used to quantify them, thus, a good action performed in a state will have high rewards; on the other hand, bad actions will be penalized. This can be used as a learning signal for the robot to improve itself. After many such episodes of trial and error, the robot would have learned the best action to perform in a given state, based on the reward. This is how learning in RL works. But we will not talk about human characters for the rest of the book. The child described previously is the agent, and their surroundings are the environment in RL parlance. The agent interacts with the environment and, in the process, learns to undertake a task, for which the environment will provide a reward.