Learn2Slither

A Q-learning agent that learns to play Snake using reinforcement learning.

Overview

Learn2Slither is a reinforcement learning project that implements a Q-learning algorithm to teach an AI agent to play the classic Snake game. The agent learns through trial and error, gradually improving its performance over multiple episodes.

Features

• Interactive visual display with pyglet
• Q-learning algorithm implementation
• Save and load trained models
• Adjustable game speed
• Training statistics

Installation

Prerequisites

• Python 3.14 or newer
• pip package manager

Setup

1. Clone the repository

   git clone <your-repo-url>
   cd Learn2Slither

2. Create a virtual environment (recommended)

   # Windows
   python -m venv venv
   venv\Scripts\activate
   
   # Linux/Mac
   python3 -m venv venv
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

Usage

Basic Command

./snake [options]

Controls (Visual Mode)

• Space: Pause/Resume
• Left Arrow: Decrease game speed
• Right Arrow: Increase game speed
• Escape: Quit game

In step-by-step mode:

• Space: Advance to next step

Available Options

Option	Description	Default
-visual on/off	Enable or disable game display	on
-episodes <int>	Number of training episodes	1
-load <file>	Load a pre-trained Q-table	None
-save <file>	Save the Q-table after training	None
-dontlearn	Disable learning (exploitation only)	False
-step-by-step	Wait for user input between steps	False
-help	Display help message	-

Example Commands

Train a new agent:

./snake -visual on -episodes 1000 -save models/my_model.csv

Load and watch a trained agent:

./snake -visual on -load models/q_table_100000.csv -episodes 10 -dontlearn

Step-by-step debugging:

./snake -visual on -load models/q_table_100000.csv -step-by-step -dontlearn

Train without visual display (faster):

./snake -visual off -episodes 10000 -save models/trained_model.csv

Learning Parameters

Configurable in config/config.py: