In order to master the algorithms discussed in this lesson, you will write code to teach an agent to play Blackjack.
Playing Cards (Source) Playing Cards (Source)
Please read about the game of Blackjack in Example 5.1 of the textbook.
When you have finished, please review the corresponding GitHub file, by reading the commented block in the BlackjackEnv class. (While you do not need to understand how all of the code works, please read the commented block that explains the dynamics of the environment.) For clarity, we have also pasted the description of the environment below:
Simple blackjack environment
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 each (player and dealer) having one face up and one
face down card.
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 (1998).
http://incompleteideas.net/sutton/book/the-book.html
Monte_Carlo.ipynb - the Jupyter notebook where you will write all of your implementations (this is the only file that you will modify!)
plot_utils.py - contains a plotting function for visualizing state-value functions and policies
Download the Exercise (Source)
If you would prefer to work on your own machine, you can download the exercise from the DRLND GitHub repository.