Snake: Python AI Game Algorithms

Understand the project

Practical overview

When it fits

Use it when a small, executable game environment is useful for comparing rule-based search with learned behavior or for studying AI implementation details.

Who it is for

Developers exploring game AI
Students learning search and reinforcement learning
Researchers comparing gameplay strategies
Readers seeking a compact executable AI experiment

Documented uses

Run a graph-search Snake agent
Play Snake with a reinforcement-learning agent
Train a reinforcement-learning model
Compare documented outcomes for two AI approaches
Study path planning, rewards and game-state decisions

Before you choose

Requirements & limits

The repository is public and marked active in the supplied record. It is written in Python and licensed under MIT. The documented setup requires Python 3.10 or later; PyTorch is additionally needed for reinforcement-learning play or training.

Choose graph search for the repository’s rule-based path-planning approach, including Hamiltonian-path and tail-safety checks.
Choose reinforcement learning to examine a neural-network agent trained through rewards and penalties with Double DQN.
The documented environment is a 6×6 grid with a maximum snake length of 36.
The repository includes a pre-trained reinforcement-learning model as well as a documented training command.
Reported example results differ between the approaches: graph search has higher documented average length and success rate than reinforcement learning in the cited README table.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up and run the documented Snake examples

Install the Python project and start its graph-search or reinforcement-learning game modes.

Before you start

Python 3.10 or later
A virtual environment
The project dependencies
PyTorch for reinforcement-learning play or training

Steps

Create and activate a virtual environment with the command below and source venv/bin/activate.
Documented command
```
python3 -m venv venv
```
Install mandatory dependencies with the command below.
Documented command
```
pip3 install -r requirements.txt
```
Run the command below to play with graph search.
Documented command
```
python3 main.py
```
Install PyTorch according to the CPU or GPU setup, then run the command below for reinforcement-learning play.
Documented command
```
python3 main.py -m rl
```
Run the command below to train a reinforcement-learning model.
Documented command
```
python3 rl_train.py
```

Expected outcome

The project is installed and its documented graph-search or reinforcement-learning mode can be started.

Guide limitations

The documented game uses a 6×6 grid.
PyTorch is an additional requirement for reinforcement-learning play and training.
The README documents commands and example results but does not establish performance beyond its reported 1,000-round examples.

Documentation 1 ↗

Evidence and transparency

Sources & review

Official repository ↗

Source record details

Source availability: Active
Sources checked: 30 September 2026
Original website title: chynl/snake · Official homepage title · retained in its source language

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