semvec/pypi-packages huggingface dataset:
mcts-catherineannie13 0.0.1
A package to run MCTS for Monopoly
Requires Python: >=3.8

# **Monopoly MCTS AI**

Monopoly MCTS AI is a Python package that implements the Monte Carlo Tree Search (MCTS) algorithm for decision-making in simulated Monopoly games. It utilizes neural networks for strategy optimization and state evaluation, providing a framework for researching AI-driven decision-making in board games.

## **Features**

- Implementation of the Monte Carlo Tree Search (MCTS) algorithm.
- Use of neural networks (NN) for evaluating game states and making decisions.
- Simulation of Monopoly game dynamics including properties, stations, utilities, and player interactions.
- Customizable strategies for AI players.

## **Installation**
### **Option 1**
Clone this repository to your local machine:

```bash
bashCopy code
git clone https://github.com/catherineannie13/Capstone-Optimising-Monopoly-Gameplay-Strategies.git
cd monopoly-mcts-ai

```

Ensure you have Python 3.8 or later installed. It's recommended to use a virtual environment:

```bash
bashCopy code
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

```

Install the required dependencies:

```bash
bashCopy code
pip install -r requirements.txt

```

### **Option 2**
Run pip install -i https://pypi.org/simple/ mcts-catherineannie13==0.0.1

## **Usage**

To run a simulation of the Monopoly game with the MCTS AI, execute the following command in the root directory of the project:

```bash
bashCopy code
python -m MonopolyBoardMCTS

```

You can customize the simulation parameters within the **`MonopolyBoardMCTS.py`** script or by modifying the command-line interface (if implemented) to adjust the number of games, AI strategies, and other settings.

## **Contributing**

Contributions to the Monopoly MCTS AI project are welcome. Please follow the standard fork-and-pull request workflow on GitHub. Ensure you write or update tests as necessary.

## **License**

This project is licensed under the MIT License - see the LICENSE file for details.

## **Acknowledgments**

- This project is inspired by the classic board game Monopoly.
- Monte Carlo Tree Search algorithm for strategic decision-making.
- Neural network implementation for state evaluation.
CorePyLib 1.0.0
A comprehensive Python library providing essential tools and utilities for streamlined development.

# CorePyLib

CorePyLib (or CPL) is a Python library that offers a collection of small, neat features to enhance your development experience.

## Features

- **Feature 1:** -
- **Feature 2:** -
- ...

## Installation

You can install CorePyLib using pip:

```bash
pip install CorePyLib
```
NEPython 1.0.6
A comprehensive Python library providing essential tools and utilities for streamlined development.

(Please note that the Testing.py file is a file used by me to test out stuff. Its not Important!)

# NEPython

NEPython (or NEPy, long: Not Enough Python) is a Python library that offers a collection of small, neat features to enhance your development experience.

## Features Overview

- **Array and Dictionary Manipulation** A sub-library to merge, sort and more.
- **More Features Soon!:** Make sure to regulary check for an Update.
```bash
pip install --upgrade NEPython
```
- ...

## Installation

You can install NEPython using pip:

```bash
pip install NEPython
```

## Features
**Array Manipulation** Merge, Sort, Shuffle, Rotate, Consecutive Sublists, Remove first Occurrence right/left, Switch Items, Most Common Item, Remove Duplicates, Seperate String and Num (WIP)