A Brief And Absolutely Incomplete Guide To Reinforcement Learning

Introduction

Reinforcement learning is a powerful and exciting way to train agents. It's a form of machine learning that enables computers to learn from their own experience in order to improve performance and achieve goals over time. Reinforcement learning algorithms have been successfully applied to many real-world applications such as computer games, robotics, language parsing, and finance trading. In this article, we'll cover the fundamentals of RL - what it is, why it's useful, how it works, a guide for dummies!

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What is Reinforcement Learning

Reinforcement learning is a sub-field of machine learning and artificial intelligence that studies how to learn using only rewards and punishments. It’s used in robotics, games, strategy-based games like chess, backgammon and Go, online advertising systems and even search engines.

Let’s say you have a robot looking around for food in an environment. If it sees the word “apple” on the wall it should go toward it (the reward), but if there isn’t any apples in sight then it should keep moving forward (the punishment). Through trial and error over time the robot will learn where to go to find apples.

Types of RL

Reinforcement learning is a type of machine learning. However, it's only a subset of machine learning. Reinforcement learning is part of the broader category known as supervised learning—it takes in data and uses it to train a model.

As you might expect, reinforcement learning gets its name from the essential concept of reinforcement: an agent receives feedback based on its actions and that feedback is used to improve future behavior (you'll learn more about this later). Lastly, reinforcement learning is also considered unsupervised because no labelled data points are required for training the model; everything is learned through trial-and-error using only observations made during interactions with its environment (the same way we learned language as babies).

Methods of Reinforcement Learning

There are many methods of reinforcement learning, but here we will focus on the main ones:

Monte-Carlo Tree Search (MCTS) - This method is used for optimizing game-playing algorithms. It uses a combination of probabilities to estimate outcomes and then uses the estimated values to determine which move to make next. The method involves using a tree search algorithm that evaluates possible moves in order to calculate their expected return values. In addition, it also considers how likely it is that an opponent will respond with certain moves based on their history of play against similar players. The algorithm then uses this information to determine which move is most likely to lead to the highest score. It will continue making moves until it reaches a point where any additional moves do not result in an improvement in score, at which point it stops and chooses the best possible score.

Deep Reinforcement Learning - Deep RL is considered a more advanced version of MCTS because it can learn from more complex input variables than just their current value and previous states; it can also use past events like reward signals or global state transitions over time from other agents in order to improve its decision making process.

Deep RL can be used to solve many different types of problems, from playing video games to controlling robots. For example, OpenAI created a deep reinforcement learning agent called DQN (Dueling Q-Network), which was able to beat humans at the game Breakout in under an hour.

Algorithms

?Reinforcement learning algorithms are a type of unsupervised machine learning algorithm. This means that you don’t need to tell your reinforcement learning algorithm what you expect it to learn. Unlike supervised machine learning algorithms, which make predictions based on labeled training data, reinforcement learning algorithms can make decisions and make predictions by themselves.

?Reinforcement learning algorithms are typically used in situations where there is no human in the loop—that is, if you want a robot or an autonomous vehicle to drive itself around without any human intervention or supervision. In this case, your goal is for the robot (or car) to learn how best to navigate its environment while avoiding obstacles and reaching its destination safely—without any instructions from humans!

Limitations of reinforcement learning.

RL is a powerful technique, but it's not without its limitations. Some of the most common ones include:

It is not good at generalizing. RL tends to work well for specific problems, but when you try to apply the same solution to other situations, the results may be unsatisfactory. For example, let's say you want your robot vacuum cleaner to clean up spilled rice on your kitchen floor. If it can learn how to sweep away all kinds of debris from all types of surfaces (e.g., carpeting or hardwood floors), then this would be an excellent result! However, if your robot vacuum cleaner only learns how to sweep up rice from one particular type of surface (e.g., tile), then it won't be very useful if there are any other kinds of spills in your house (like nuts & berries).

It is not good at learning from small numbers of examples. This problem affects both supervised and unsupervised learning techniques: supervised methods require labeled training data before they can start making predictions; unsupervised methods require large amounts of unlabeled data before they can identify important correlations between variables in the dataset(s). A typical RL algorithm needs millions or billions of examples before it can begin learning anything useful about its environment -- which unfortunately isn't always possible because there might simply not be enough data available for each task we want our AI system perform well!

Real World Applications of Reinforcement Learning

Real-world applications of reinforcement learning can be found in a wide range of fields.

In robotics, for example, robots use reinforcement learning to learn how to get through various tasks by trial and error. This is useful because it helps them figure out how to perform their tasks much more quickly than if they were programmed with instructions.

In finance, a very high percentage of trading is automated using reinforcement learning models. These models use historical data to predict what will happen next in the market, so that they can make trades on behalf of investors based on what they think will happen next. The goal is always to make more money than the investor would have made without the model's help.

Self driving cars are another example of a system that uses reinforcement learning. These cars learn how to drive by trial and error as they're driven around by their human owners. The goal is always to avoid collisions, which means that the car gets better at driving over time.

Because RL is being applied to a wide range of markets and applications, the demand for ML Engineers with an RL background is going to continue to grow as the world automates. Follow Cubiq Recruitment for AI & ML jobs, industry insights and confidential friendly career advice.