Month: January 2016

Hello,

I have recently made a small Python port of my GPU NeoRL library. It doesn’t have the same features, so the most important differences are listed here:

• It is fully connected (not sparsely connected)
• It uses a new method for organizing temporal data (predictive coding)
• It is slower
• It is much easier to understand!

I call this port MiniNeoRL, since it serves mostly to help me prototype new algorithms and to explain the algorithms to others. Now I am not exactly a Python expert, but I think the code is simple enough such that with some explanation it should be easy to understand.

Along with MiniNeoRL I have made this slideshow that serves as a brief overview of what NeoRL is and how it works:

NeoRL_presentation

Until next time!

Hello,

In my ongoing quest of improving NeoRL to produce a generic intelligent reinforcement learning agent, I created a demo I would like to share. It’s a simple demo, but still interesting in my opinion.

The agent receives 1D vision data (since the game is 2D), and must drive a car around a thin track. This is essentially a “thread the needle” task, where the AI requires relatively precise control in order to obtain reward.

As a human, I was not able to make it as far as the AI did. The AI almost completed the entire track, I was only able to make it about half-way. It looks easy, but it’s not!

Training time: About 2 minutes.

Here is a video of the car being controlled by NeoRL:

Until next time!