https://github.com/rvats20/um-game-playing-strength-of-mcts
MCTS is a widely used search algorithm for developing agents that can play board games intelligently. Over the past two decades, researchers have proposed dozens, if not hundreds, of MCTS variants. Despite this, it's been challenging to determine which variants are best suited for specific types of games.
https://github.com/rvats20/um-game-playing-strength-of-mcts
agents algorithms algorithms-and-data-structures generative-ai machine-learning python3
Last synced: 5 months ago
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MCTS is a widely used search algorithm for developing agents that can play board games intelligently. Over the past two decades, researchers have proposed dozens, if not hundreds, of MCTS variants. Despite this, it's been challenging to determine which variants are best suited for specific types of games.
- Host: GitHub
- URL: https://github.com/rvats20/um-game-playing-strength-of-mcts
- Owner: rvats20
- License: mit
- Created: 2024-11-16T07:24:26.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-28T17:34:35.000Z (12 months ago)
- Last Synced: 2025-03-31T11:32:28.429Z (8 months ago)
- Topics: agents, algorithms, algorithms-and-data-structures, generative-ai, machine-learning, python3
- Language: Jupyter Notebook
- Homepage: https://rvats20.github.io/portfolio/
- Size: 206 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# UM-Game-Playing-Strength-of-MCTS
Welcome to the **UM-Game-Playing-Strength-of-MCTS** repository! This project is part of a Kaggle-hosted competition aimed at evaluating the game-playing strength of Monte Carlo Tree Search (MCTS) algorithms.
## Table of Contents
- Introduction
- Competition Details
- Project Structure
- Installation
- Usage
- Contributing
- License
- Contact
## Introduction
Monte Carlo Tree Search (MCTS) is a heuristic search algorithm for decision processes, most notably employed in game playing. This repository contains the code and resources used to participate in the Kaggle competition focused on assessing the performance of MCTS in various game scenarios.
## Competition Details
- **Host**: Kaggle
- **Objective**: Evaluate the effectiveness of MCTS algorithms in game playing.
- **Dataset**: Provided by Kaggle, includes various game scenarios and outcomes.
- **Evaluation Metric**: Performance of the MCTS algorithm in terms of win rate and computational efficiency.
## Project Structure
```
UM-Game-Playing-Strength-of-MCTS/
├── data/
│ ├── raw/
│ ├── processed/
├── notebooks/
│ ├── EDA.ipynb
│ ├── MCTS_Implementation.ipynb
├── src/
│ ├── data_preprocessing.py
│ ├── mcts_algorithm.py
│ ├── evaluation.py
├── tests/
│ ├── test_mcts.py
├── README.md
├── requirements.txt
├── setup.py
```
## Installation
To get started with this project, clone the repository and install the required dependencies:
```bash
git clone https://github.com/yourusername/UM-Game-Playing-Strength-of-MCTS.git
cd UM-Game-Playing-Strength-of-MCTS
pip install -r requirements.txt
```
## Usage
1. **Data Preprocessing**: Prepare the dataset for training and evaluation.
```bash
python src/data_preprocessing.py
```
3. **Run MCTS Algorithm**: Execute the MCTS algorithm on the prepared data.
```bash
python src/mcts_algorithm.py
```
4. **Evaluate Performance**: Assess the performance of the MCTS algorithm.
```bash
python src/evaluation.py
```
## Contributing
We welcome contributions from the community! If you have any suggestions or improvements, please feel free to submit a pull request or open an issue.
## License
This project is licensed under the MIT License. See the LICENSE file for more details.
## Contact
For any questions or inquiries, please contact yourname.