This is a series of projects where I solve AI gym environments by building RL algorithms from scratch using Python, Pytorch and Tensorflow
Use Monte Carlo methods to evaluate a policy in reinforcement learning in Blackjack
Blackjack is a card game where the goal is to obtain cards that sum to as near as possible to 21 without going over. They're playing against a fixed dealer. Face cards (Jack, Queen, King) have point value 10. Aces can either count as 11 or 1, and it's called 'usable' at 11. This game is placed with an infinite deck (or with replacement). The game starts with dealer having one face up and one face down card, while player having two face up cards. ( Virtually for all Blackjack games today). The player can request additional cards (hit=1) until they decide to stop (stick=0) or exceed 21 (bust). After the player sticks, the dealer reveals their facedown card, and draws until their sum is 17 or greater. If the dealer goes bust the player wins. If neither player nor dealer busts, the outcome (win, lose, draw) is decided by whose sum is closer to 21. The reward for winning is +1, drawing is 0, and losing is -1. The observation of a 3-tuple of: the players current sum, the dealer's one showing card (1-10 where 1 is ace), and whether or not the player holds a usable ace (0 or 1).
This environment corresponds to the version of the blackjack problem described in Example 5.1 in Reinforcement Learning: An Introduction by Sutton and Barto. http://incompleteideas.net/book/the-book-2nd.html
The observation of a 3-tuple of: the players current sum, the dealer's one showing card (1-10 where 1 is ace), and whether or not the player holds a usable ace (0 or 1).
The player can request additional cards (hit=1) until they decide to stop (stick=0) or exceed 21 (bust).
https://github.com/openai/gym/blob/master/gym/envs/toy_text/blackjack.py