Reinforcement learning is one of the hottest buzzwords in the IT industry and its popularity is only growing every day. An artificial intelligence technique that is now being widely implemented by companies around the world, reinforcement learning is mainly used by applications and machines to find the best possible behavior or the most optimum path in a specific situation. From robotics to marketing automation, gaming, and aircraft control, it is now being used in a number of verticals 

In this article, we will be discussing everything there is to know about reinforcement learning

What is Reinforcement Learning?

Reinforcement learning is a type of machine learning algorithm where the machine learns in an interactive environment through trial and error by using the feedback of its own actions and experiences. It uses rewards and punishments as the main signals for positive and negative behavior. These algorithms are based on the idea of reward hypothesis (where the algorithm gets a reward for every right action) and the goal is to maximize the cumulative rewards. Deep reinforcement learning algorithms can continuously adapt to the environment to maximize the reward in the long term, depending on the complexity of the problem. 

A machine or a robot using reinforcement learning will try different ways to solve the same problem, gather feedback regarding the success of each action, and then adjust its strategy until it is able to optimally solve the problem with high accuracy. To be able to compute actions and get the right feedback, it needs a lot of input data.

Depending on the feedback, an action either gets positive or negative reinforcement. If the action leads to an acceptable outcome, then it gets a positive reinforcement, otherwise, it gets a negative reinforcement. With positive reinforcement, the frequency of that action increases, but with negative reinforcement, the frequency of that action actually decreases. 

Here are the main characteristics of reinforcement learning algorithms: 

  • There is no programmer giving feedback, only reward signal
  • The input is an initial state from which the model starts 
  • There can be many possible outputs since there are a number of solutions for many problems 
  • The decision making is sequential
  • Feedback is not instant, it is always delayed
  • The machine’s actions determine the subsequent data that it receives

Training a Reinforcement Learning Algorithm

When a reinforcement learning algorithm is trained, there are four different factors involved — initial state, new state, actions, and rewards.

To understand it better, let’s consider an example of a video game where the goal of the AI is to make it to the end of the level by moving across the screen. In this case, the initial state would be the first frame of the game and based on that state the AI would be required to take an action. 

While during the initial training phase, the actions will mostly be random, the model will steadily get reinforced through rewards and some of the actions will become more common. After every action, a new state is created. 

If the action taken is desirable, then the system is rewarded for it and the action becomes more common. So, in the context of the game if the character is able to stay alive and not get hit by an enemy after taking a particular action, then that action is positively rewarded and the AI is more likely to perform the same action in the future.

Applications of Reinforcement Learning 

While reinforcement learning is still a rather new technology, companies around the world are beginning to rely on it for solving problems where sequential decision making is needed. The idea is to implement reinforcement-learning where human-based decision making can be automated. 

Here are some of the many applications:

1- Robotics

With deep reinforcement learning, you are able to use a framework and a set of rules for otherwise hard to engineer behavior. It also helps robotic machines grow exponentially and gather more intelligence since reinforcement learning doesn’t require any supervision.  

2- Medicine

As an artificial learning technique, reinforcement learning is suitable for figuring out the correct treatment plan for patients based on their current health conditions and drug therapies. It can also be used in clinical trials as well as other health applications.

3- Chatbots

Reinforced learning can train chatbots through trial and error conversations either with the help of a rule-based user simulator or real user interactions. By analyzing text conversations that perform better than the others, chatbots can serve customers in a more effective way. 

4- Personalization

Reinforcement learning algorithms can offer a more personalized experience to customers by identifying the right targeted ads and recommendations for their next purchase. 

5- Games 

The very first and popular implementation of reinforced learning was done in AlphaGo, artificial intelligence-powered by reinforcement learning, which beat the three-times reigning European Champion of the complex board game Go by 5 points to 0. The machine was also able to beat the 18-time world champion, Lee Sedol, in a five-game Go match. 

It was the first time that an AI system using deep reinforcement learning techniques was able to beat a human at such a and sophisticated game. Go is an abstract strategy board game that is played between two players with black and white stones. It was first invented in ancient China over 2,500 years ago. 

Reinforcement machine learning has a huge potential and it is being steadily implemented by companies across the world, which has also increased hiring opportunities in the sector. Springboard offers a dedicated machine learning career track program that is 1:1 mentoring-led, project-driven along with a job guarantee and can make you an expert machine learning algorithm in just 6 months.