For any Reinforcement Learning endeavor, it is best to learn through simple games like Blackjack because they have only a few uncertainties to keep track of, clear reward and punishment rules, and a straightforward goal to aim for.Ĭasino games like Blackjack are designed to statistically favor the “house,” and I am interested in seeing if I can “crack” that through Reinforcement Learning algorithms. The purpose of this post is to show the dazzling power of even simple simulations in modeling the world around us. There are more granular rules to Blackjack, read them here. Otherwise, the player wins if they are closer to 21 than the dealer. If the player has exactly 21, they automatically win. If the player exceeds 21, they “bust” and automatically lose the round. They can also choose to “stand” and keep the cards they have. If the player has less than 21, they can choose to “hit” and receive a random card from the deck. The goal of the game is to get the value of our cards as close to 21 as possible, without crossing 21.
The player can only see one of the dealer’s cards. At the start of a round, both player and dealer are dealt 2 cards. Blackjack is a card game played against a dealer.
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