A summary of years of experience that might help you save time — Don’t just learn, experience. Don’t just read, absorb. Roy T. Bennett, The Light in the Heart Introduction First, a disclaimer: what works for some, does not necessarily work for others. In this article, I expose what worked for me after I spent years reading academic papers, books, and source codes related…

Tensorflow.js implementation of DQN in Reinforcement Learning — “Practice what you know, and it will help to make clear what now you do not know” Rembrandt Overview The Deep Q-Network proposed by Mnih et al. [2015] is a jumpstart and building point for many deep reinforcement learning algorithms. …

Pushing the limits of MC method with special cases and practical examples. — Monte Carlo, as well as Dynamic Programming, Temporal Difference are the main methods for starters in Reinforcement Learning. Monte Carlo First, let’s have a brief reminder of what is Monte Carlo method. Monte Carlo is an algorithm that generates paths (which constitutes an episode) based on the current policy which usually splits between…

Overview to Reinforcement Learning newcomers Reinforcement Learning is an amazing field, it gives a sensation of magic. However, it is not an easy topic to study, it is hard, it is frustrating, especially for newcomers. RL-Lab.com has embarked on a mission to make it easier for people to practice and…

A smooth start in Reinforcement Learning — The following article is about my RL-Lab idea to make Reinforcement Learning an easier topic to learn. It is the culmination of several years of experience in the field. The Context After several years of involvement in Reinforcement Learning, I have come to the conclusion that no matter how much you study…

Introduction to CFR and implementation of the Tic-Tac-Toe game — “We should regret our mistakes and learn from them, but never carry them forward into the future with us.” Lucy Maud Montgomery Learning from regrets is what Counter Factual Minimization is all about. The notion of “regret” is introduced in the article “Introduction to Regret in Reinforcement Learning”. However, it…

Sometimes regretting is a good way to improve — Update: The best way of learning and practicing Reinforcement Learning is by going to http://rl-lab.com “In the end, we only regret the chances we didn’t take” Lewis Carroll Introduction It is almost sure that every human has regretted something (actually many things) during his/her lifetime. Regretting not to buy a ticket…

A concrete implementation of Neural Fictitious Self-Play in Leduc Hold’em Poker Game — Update: The best way of learning and practicing Reinforcement Learning is by going to http://rl-lab.com This article describes an implementation of Neural Fictitious Self-Play (NFSP) in Leduc Hold’em Poker Game, based on code by Eric Steinberger. The full source code can be found on his Github repository. If you are…

A gentle approach for developers to decipher the math formula of Reinforcement Learning — Update: The best way of learning and practicing Reinforcement Learning is by going to http://rl-lab.com If you are a developer with not enough knowledge in math, you might be having hard time grasping the basic formula of Reinforcement Learning.