Literature

Literature card game implementation: https://en.wikipedia.org/wiki/Literature_(card_game)

Setup

Install with pip install literature. Built for Python 3.6.0.

Example gameplay:

>>> from literature import get_game, Card, Suit
>>> import logging
>>> logging.basicConfig(level=logging.INFO)
>>> l = get_game(4)
>>> l.turn
Player 3
>>> l.players[3].hand_to_dict()
Suit.CLUBS: [A of C, K of C]
Suit.DIAMONDS: [2 of D, 10 of D, J of D, Q of D, K of D]
Suit.HEARTS: [A of H, 5 of H, J of H]
Suit.SPADES: [A of S, Q of S]
>>> move = l.players[3].asks(l.players[2]).to_give(Card.Name(3, Suit.DIAMONDS))
>>> l.commit_move(move)
INFO:literature.literature:Failure: Player 3 requested the 3 of D from Player 2

Play against a model that I trained with:

>>> import literature
>>> import logging
>>> logging.basicConfig(level=logging.INFO)
>>> literature.learning.play_against_model('literature/model_10000.out')

See literature.py for documentation.

Limitations

The bots only consider asking for a Card that they know a Player does not possess in the case that there are no other possible Moves. I made this simplification because the initial training took too long otherwise.
The game state for a given Player encodes what that Player knows that all other Players know about each other's hands, but I don't encode any levels further than that. For example, the game state for Player i doesn't encode what Player j knows that Player k knows that Player l knows.
- I chose not to represent this because it vastly increases the dimensionality of the problem, and I don't think that the information is particularly valuable.
During training, the bots will occasionally get caught in an infinite loop. To mitigate this, I add noise to the scores for each move and kill games after 200 moves.
I'm only training the bots for games of four right now. The code can be easily adapted to work for a different number of players.

neelsomani/literature

Literature

Setup

Limitations