https://github.com/neelsomani/literature
Literature card game implementation: https://en.wikipedia.org/wiki/Literature_(card_game)
https://github.com/neelsomani/literature
artificial-intelligence card-game machine-learning neural-network q-learning
Last synced: about 1 year ago
JSON representation
Literature card game implementation: https://en.wikipedia.org/wiki/Literature_(card_game)
- Host: GitHub
- URL: https://github.com/neelsomani/literature
- Owner: neelsomani
- License: mit
- Created: 2019-06-10T05:37:38.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2024-06-17T23:09:41.000Z (almost 2 years ago)
- Last Synced: 2025-03-08T04:36:24.054Z (over 1 year ago)
- Topics: artificial-intelligence, card-game, machine-learning, neural-network, q-learning
- Language: Python
- Homepage:
- Size: 14.9 MB
- Stars: 10
- Watchers: 2
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# 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.