For our final project we are modeling blackjack as a game for Artificial Intelligence agents to learn, and to test those agents performance against established 'optimal' strategies found online. We aim to implement an expectimax agent, a q-learning agent, a random agent, and maybe more. Additionally, a user can try their own hand by playing the dealer via a command-line interface
The game is written in Python 3. Make sure you have Python 3 installed!
To play against the dealer, simply run python3 blackjack.py, and the game will guide you from there!
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-a,--agent_type: Option for a string representing the type of agent to play- One of 'user' (default), 'expectimax', 'random', 'q-learning', 'optimal'
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-n,--hands: Option for an integer for the maximum number of hands to play (doesn't apply for user agents)- Default to 0, should be specified every time
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-s,--starting_money: Option for an integer for how much money the player starts off with- Default to 1000
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-v,--verbose: Option for a boolean for the game to be played with verbosity- Default to false for all agents other than the user agent, which needs verbosity to play
- Verbosity will print every deal, every action, etc, and shouldn't be used for playing many hands with an artificial agent unless you want endless text output
- One of 'True' or 'False'
- Play blackjack on your own with 100 dollars to start
python3 blackjack.py -s 100
- Have the 'optimal' agent (hardcoded strategy from online) play blackjack for 10,000 hands with $1 million to start
- `python3 blackjack.py -a optimal -n 10000 -s 1000000
- No doubling down after splitting
- Splitting after splitting OK
- Can re-split aces
- Allowed to split aces like a normal split
- Splits must be same card, not same value
- Some would let you split Jack, Queen. We can only split Jack, Jack
- Dealer stands on soft 17
Online calc says this would make house edge in 8 deck blackjack just .502, and -.06 for 1 deck game.