Neurabox [v1.1]: Neural Network Machine Learning & NEAT Library

Neurabox is a Roblox Neural Network and Machine Learning library. It ships with 5 well-known optimizers, fully implemented feedforward neural network classes, and a mostly complete NEAT implementation!

What is this library?

For those unfamiliar with the concept of Machine Learning, it's essentially the process of a virtual algorithm learning to complete a task, and in most cases, is used with a Neural Network, which is an extremely powerful paradigm inspired by biological brains, structured with neurons and synapses.

One of the stand-out features of this library is NEAT.

NEAT

NEAT refers to Neuro-Evolution of Augmented Topologies, which describes the process of teaching neural networks through the use of mutations and natural (or artificial) selection. Unlike other genetic algorithms, which merely tweak the strength of connections in the network, NEAT evolves the network topology and architecture, effectively being able to create, remove, and modify neurons and synapses, which is very powerful, and allows for highly specialized network structures for any task at hand.

Q-Learning

Q-Learning is a simple reinforcement learning algorithm that performs efficiently with discrete, low-dimensional tasks. It can perform very well practically depending on your environment.

Documentation

To learn about how to use this library, along with a basic explanation of neural networks, and example code, just visit the Neurabox Documentation

Download

You can download Neurabox here: Neurabox Model Page

Side Notes

This is my first published Roblox library, so I'm sure it may be flawed, but I still hope that this library may help those of you interested in training neural networks in Roblox. Don't hesitate to give me feedback or bug reports in the replies! Feedback is greatly appreciated and I'll take it into consideration. In the future, I'll likely be making a GitHub for this library so bug reports can be solved there instead.
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[Update v1.1]

  • Added Q-Learning
  • Resolved some minor performance bottlenecks