Programmatically recreating Sol LeWitt’s “All two-part combinations of arcs from corners and sides, and straight, not straight and broken lines” with d3.js

Skip to the full page interactive version if you don’t want to read the post!

I recently went to MASS MoCA with my friends for the first time. I had heard of it, but assumed they were talking about a huge chocolate flavored coffee drink. So imagine my surprise! read more

Descending into modular neuroevolution for logic circuits

A while ago, I did a post on beating OpenAI games using neuroevolution (NE). Go read that if you’re interested, but here’s the gist: a typical strategy for training an agent to beat those games is to have a neural network (NN) play the games a bunch, and then improve the weights of the NN using a reinforcement learning algorithm that uses gradient descent (GD), and it of course works pretty well.

However, an alternative to those methods is to use a gradient free method (which I’ll call “GD-free”), like I did in that post: you try a bunch of random changes to the NN’s weights, and only keep the resulting NNs that play the game well. That’s the “evolutionary” aspect of it, and using methods like that to create NNs is often called “neuroevolution” (NE). read more

Training a real robot to play Puckworld with reinforcement learning

After I trained an agent to play “puckworld” using Q-learning, I thought “hey, maybe I should make a real robot that learns this. It can’t be that hard, right?”

Hooooooooo boy. I did not appreciate how much harder problems in the physical world can be. Examples of amateurs doing Reinforcement Learning (RL) projects are all over the place on the internet, and robotics are certainly touted as one of the main applications for RL, but in my experience, I’ve only found a few examples of someone actually using RL to train a robot. Here’s a (very abridged!) overview of my adventure getting a robot to learn to play a game called puckworld. read more

Beating OpenAI games with neuroevolution agents: pretty NEAT!

Let’s start with a fun gif!

Something I’ve been thinking about recently is neuroevolution (NE). NE is changing aspects of a neural network (NN) using principles from evolutionary algorithms (EA), in which you try to find the best NN for a given problem by trying different solutions (“individuals”) and changing them slightly (and sometimes combining them), and taking the ones that have better scores. read more