Homage

Velora comes packed with a variety of packages and builds on a wealth of knowledge that have been fundamental to its creation.

This page acts as a homage to show our and appreciation for the libraries and researchers that have helped make its packages possible.

Packages

• PyTorch for model creation
• Pydantic for data validation
• Gymnasium for environment management
• Poetry for package management
• Pytest for unit testing

Research Papers

We are thankful for a LOT of researchers in the RL space, more than we can count! 😅

While their names are not mentioned here, their papers are outlined throughout the documentation accompanied with the algorithm implementations associated to their work.

Instead, we want to dedicate this section to some special mentions that the framework centres around: Liquid Neural Networks and Neural Circuit Policies.

Liquid Neural Networks

Liquid Time-Constant Networks

Interpretable Recurrent Neural Networks in Continuous-Time Control Environments

Closed-Form Continuous-Time Neural Networks

Neural Circuit Policies

Neuronal Circuit Policies

Neural Ordinary Differential Equations

A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits