https://github.com/abhigyan126/rock-paper-scissors-
An Q learning based Rock Paper Scissors game with single and multiplayer
https://github.com/abhigyan126/rock-paper-scissors-
Last synced: 3 months ago
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An Q learning based Rock Paper Scissors game with single and multiplayer
- Host: GitHub
- URL: https://github.com/abhigyan126/rock-paper-scissors-
- Owner: Abhigyan126
- Created: 2025-02-12T08:32:52.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-12T08:45:47.000Z (8 months ago)
- Last Synced: 2025-02-12T09:44:28.287Z (8 months ago)
- Language: Python
- Size: 40 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Rock Paper Scissors with Q-Learning AI
This repository contains a Rock Paper Scissors game implemented using Pygame, featuring a Q-Learning AI opponent. You can play against the AI in single-player mode or against another human player in multiplayer mode.
## Introduction
This project provides a fun and interactive Rock Paper Scissors game. The single-player mode utilizes a Q-Learning AI that learns to play better over time. The AI adapts its strategy based on previous games, making it a challenging opponent. The multiplayer mode allows two human players to compete against each other.
## Features
- **Single-player mode:** Play against a Q-Learning AI.
- **Multiplayer mode:** Play against another human player.
- **Graphical user interface:** Uses Pygame for a visually appealing experience.
- **Q-Learning AI:** The AI learns and improves its strategy over time.
- **Score tracking:** Keeps track of the score for each player.
- **Game reset:** Easily reset the game to start a new match.
- **Draw detection:** Detects and announces draws.
- **Menu System:** Allows selection between singleplayer and multiplayer modes.
## How to Run
1. **Clone the repository:**
```bash
git clone https://github.com/Abhigyan126/Rock-Paper-Scissors-.git
cd Rock-Paper-Scissors-
```
2. **Install dependencies:**
```bash
pip install pygame numpy
```
3. **Run the game:**
```bash
python main.py
```
## Training the AI
The AI's knowledge is stored in `q_table.pkl`. Each game played will update the Q-table, improving the AI's strategy. The AI's learning is continuous; every time you play against it, the AI learns. No separate training script is needed. Just play the game in single-player mode, and the AI will gradually become more proficient.
## Game Logic
- The game is played in rounds (best of 3).
- The player chooses Rock, Paper, or Scissors by clicking on the corresponding image.
- In single-player mode, the AI chooses its move after the player's choice.
- The winner of each round is determined based on the classic Rock Paper Scissors rules.
- The score is updated after each round.
- The game ends after three rounds.
- The player with the highest score wins the game.
- A draw is declared if both players choose the same option in the final round.
## Dependencies
- Pygame
- NumPy
- pickle (built-in Python library)