Blackjack is one of the most popular casino games in the world even though it is inherently unfair towards the player and strongly favors the “house”. In order to still make a profit, professional Blackjack players resort to a strategy called card counting. In this project, I combined card counting with reinforcement learning and developed an agent that is able to play Blackjack with a positive net return consistently. The final agent staunchly outperforms human players.

Repository: https://github.com/Yahnnosh/Implementation-of-a-Blackjack-playing-agent