2048 AI Game

This project implements an AI agent that learns to play the popular game 2048 using reinforcement learning techniques.

Project Overview

The 2048 AI Game consists of several components:

1. A game engine that simulates the 2048 game
2. A deep Q-learning network (DQN) model for the AI agent
3. A training controller that manages the learning process
4. A user interface for human players

The AI agent learns to play the game by repeatedly playing and improving its strategy based on the rewards it receives.

Sneak peek at the UI

Cool background stickers used in the UI are taken from the Hack Club website.

File Structure

• game.py: Contains the Game class that implements the 2048 game logic and rendering.
• model.py: Defines the DQN (Deep Q-Network) and DinoGamer classes for the AI agent.
• controller.py: Manages the training process for the AI agent.
• game_user.py: Allows human players to play the game.

Requirements

• Python 3.7+
• PyTorch
• Pygame
• NumPy

Installation

1. Clone this repository:

   git clone https://github.com/artem4852/2048.git
   cd 2048
   

2. Install the required packages:

   pip install torch pygame numpy
   

Usage

Training the AI

To train the AI agent, run:

python controller.py

If you want to override trained weights, ensure that training is set to True in the file controller.py on line 5.

This will start the training process. The agent will play multiple games, learning and improving its strategy over time. Training progress and statistics will be displayed in the title of the window.

Having AI play the game

Repo includes some pretrained weights. To see AI playing ensure that training is set to False in the file controller.py on line 5.

Then, run:

python controller.py

Playing yourself

To play the game yourself, run:

python game_user.py

Use the arrow keys or WASD to move the tiles.

AI Model

The AI uses a Deep Q-Network (DQN) implemented in PyTorch. The network architecture consists of several fully connected layers with ReLU activations and dropout for regularization.

Key features of the AI implementation include:

• Experience replay
• Target network for stable learning
• Epsilon-greedy exploration strategy
• Learning rate scheduling

Customization

You can adjust various parameters in the model.py and controller.py files to experiment with different learning strategies:

• Learning rate
• Epsilon decay rate
• Replay memory size
• Network architecture

License

This project is open source and available under the MIT License.