Hello, We have applied our OgmaNeo2 online/incremental learning software to the problem of robotic quadruped control. We learned a slightly faster policy than the hand-coded one using reinforcement learning (RL). For this experiment, we used the Stanford Pupper robot designed by the Stanford Robotics Club – and made use of its hand-coded policy as a…
Hello all, It’s time for us to finally show off our Atari Pong demo! Our Sparse Predictive Hierarchies (SPH, as implemented in OgmaNeo) are now able to play Atari games. Our first test is Pong, a test of reinforcement learning from pixel data. If you need a refresher on how the prediction-only version of OgmaNeo2…
Hello! In the previous post, we demonstrated a self-driving model car that performed all processing on-board on a Raspberry Pi 3. We have now released the code for the car and associated tools. The following repositories are available on GitHub: EOgmaNeo – the primary library containing our super-fast online learning technology! EOgmaDrive – a description…
Hello! We have been hard at work to create (to our knowledge) the world’s first fully online learning self-driving mini-car! Using a stock RC car model, we equipped it with a Raspberry Pi 3 along with an Arduino to control the servos/speed controller. We used the latest iteration of our online learning software, EOgmaNeo, to…
November 2016 – Ogma is pleased to announce the release of its OGMANEO software library, alongside usage examples and demonstrations, and language bindings for Java™ and Python™. The OgmaNeo library contains implementations of Online Predictive Hierarchies, as described in the paper: “Feynman Machine: The Universal Dynamical Systems Computer” (http://arxiv.org/abs/1609.03971). In September, our paper was listed as among…