Note, the following articles will not be the usual step-by-step coding tutorials. Instead, we will focus more on acquiring a conceptual understanding by diving into the math of Q-learning in a three-part series. That being said, a basic understanding of probability and Markov chains will be useful in order to understand the following concepts. If you aren’t familiar with these concepts, watch this MIT series to get up to speed!

Usually, when I program a Machine Learning algorithm, I’m bothered by the fact that I don’t know the fundamental concepts behind the artwork. Sure I will be able to explain…

Learning through pure randomness shouldn’t make any sense.

But it works…

Using some statistics magic, you can calculate unknown values through randomness with great accuracy and solve complex problems including Reinforcement Learning problems. One of these methods of “random learning” is called Monte Carlo Simulation.

In this article, we are going to understand Monte Carlo (MC) methods applied to Reinforcement Learning. Alongside the explanations, we are going to apply this algorithm to play the game of blackjack!

Part 1 Monte Carlo RL Tutorial

Applying MC Learning onto Blackjack Open AI Gym


  • What is Monte Carlo Learning
  • Dynamic Programming vs MC Learning
  • Policy Iteration
  • MC Prediction
  • MC…

In the U.S, 350+ Sanitization workers in New York died in one week due to hazardous working conditions given the pandemic situation. In Canada, sanitization workers in schools have now moved from working every other day to now twice daily to ensure the health and priority of the teachers and students.

However, what about the health of the Sanitization workers? What about those who are risking their lives to clean the environment that could have a high possibility of contamination in order to protect other people.

After conducting a poll in the United States, 80% of sanitization workers felt unsafe…

So, we know what regression models are: take a dataset and create a model that will accurately predict the location of new points added to the dataset. Simple.

Note, this article focuses more behind the mathematics of linear regression. You will need to understand the basics of partial derivatives and some linear algebra. If you are not too comfortable with these topics, bookmark this article, watch some Khan Academy videos and then have a read. You’ll thank me later.

Have you ever felt like you never properly understood gradient descent, regression or loss functions? Did you brush these concepts aside and focus all on the coding? Yeah, me too.

To understand what’s going on behind the algorithms, we must consider the math. I’ll be honest; machine learning mathematics…

Tawsif Kamal

A 17 y/o Reinforcement Learning enthusiast looking to leverage tech towards driving social change 🤖

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