# plAIer
A Python library that uses a reinforcement learning AI to play board games such as Chess, Tic Tac Toe, and Reversi.
You can also find the project on [PyPI][]
[PyPI]: https://pypi.org/project/plAIer/
## Example of use
```python
# The current game state :
gameState = """
X | O |
---+---+---
O | O | X
---+---+---
X | | O
"""
# Import the library :
import plAIer
# Create database if not exists :
plAIer.createDatabase("database_filename.json", "database_name", "description", ["outcomes", "list"])
# Initialize the AI :
outcomesRating = {"O won": 1, "tie": 0, "X won": -1} # The definition of outcomes allows us to determine what the goals of the AI are.
AI = plAIer.Game("database_filename.json", outcomesRating)
# Find the best move in the current game position :
bestMove = AI.findBestMove([
{"move" : "[0, 2]",
"stateAfterMove": "\n X | O | O\n---+---+---\n O | O | X\n---+---+---\n X | | O\n"},
{"move" : "[2, 1]",
"stateAfterMove": "\n X | O | \n---+---+---\n O | O | X\n---+---+---\n X | O | O\n"}
])
print(eval(bestMove)) # Output : [2, 1]
```
### Database format
Databases are JSON files which follow this format :
```json
{
"name": "name of the game",
"description": "additional informations",
"outcomes": ["list", "of", "outcomes"],
"data": {"gameState1": {"outcome1": 1234, "outcome2": 2345, "outcomeN": 4567},
"gameState2": {"outcome1": 5678, "outcome2": 6789, "outcomeN": 7890},
"gameStateN": {"outcome1": 8901, "outcome2": 9011, "outcomeN": 0}
}
}
```
## How to get it
Write on your terminal (bash or Windows cmd/powershell) the following command :
```bash
pip install plAIer
```
## Contribute
Don't hesitate to contribute with pull requests :
1. Fork the repository
2. Do your commits
3. Add your pull request on the repository with a description of what your pull request does
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