Introduction to Reinforcement Learning

• There are three main groups of algorithms in ML:

1. Unsupervised Learning
   • Is used for discovery of new patterns.
   • For example, clustering of customers into groups based on their similarities. The core principle here is that the resulting groups did not exist prior, but rather are suggested by the machine in the process.
2. Supervised Learning
   • Is when we teach a machine to search and identify patterns that we have seen before.
   • For example, classification of pictures of dogs and cats into the two categories “dogs” and “cats”. First, we show the algorithm thousands of already labelled images so it can extract features that are essential to dogs and features that are essential to cats. After this the algorithm will be able to categorise new images as either of “dogs” or of “cats”.
   • The difference of this approach to unsupervised learning is that we have to first provide the labelled data for the algorithm to learn.
3. Reinforcement Learning (RL)
   • RL is an area of ML where an imaginary agent is being presented with a problem and is being rewarded with a “+1” for finding a solution to the problem or punished with a “-1” for not finding a solution.
   • Unlike with Supervised Learning, the agent is not given instructions on how to perform the task. Instead, it performs random actions and interacts with its environment. It learns through trial and error which actions are good and which actions are bad.

Advantages of Reinforcement Learning

1. It doesn’t require large labelled datasets.
2. RL is innovative. RL algorithms can come up with entirely new solutions that were never even considered by humans.
3. RL is bias-resistant.
4. On-line learning. RL runs in real-time and combines exploration and exploitation. This means that it can bring results while improving at the same time. Other algorithms would require re-training and re-deployment.
5. RL is goal-oriented.
6. RL can be used for a sequence of actions. Ex. robots playing soccer or self-driving cars getting to their destinations.

Use-Cases in Marketing:

1. Creaing personalized recommendations
2. Optimizing Advertisements Budgets
3. Selecting the best content for Advertisements
4. Increasing customer lifetime value
5. Prediciting customer responses to price plan changes