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

In case hexapods weren’t creepy enough: the centipede robot!

Similar to…most of? my ideas, I don’t remember why I thought of this. I think after I made the reinforcement learning robot, I was on a robot kick, and came up with this. Hexapods are of course a robot classic, but I don’t think I had ever seen a centipede robot.

Why a centipede? Well… I can make up a few “practical” reasons: because of its length, it could potentially bridge gaps, or bend “upwards” to have height, or possibly even climb. But the real reason is because they haven’t been done that much and I thought it would be cool, funny, and creepy. read more

The Mostly Printed CNC (MPCNC), mostly

I’m not sure when I got the urge to make a CNC… or maybe it was always there. I did a summer job in a machine shop when I was 19, where I was given a minimum of training by the 88 year old machinist: “keep the pink things [wiggling his fingers] away from the sharp things [pointing to the milling machine’s cutting edge], boy.”

(I’m not joking. He did show me more things later, but only because I asked. He would mostly just motion for me to come over so he could tell me filthy jokes from the silent film era and cackle to himself.) read more

Chile: hilly, Will-y, thrilly, and, uh…chilly

I just got back from a trip to Chile with my friends! We were visiting our friend Will. After defending, he did a pretty similar trip to the one I did after defending, but the South American version instead of Southeast Asia. I believe he started in Peru, then went through Bolivia and met us in Santiago, Chile.

It worked out fairly perfectly, because I think he was reaching the same level of “travel done-ness” that I did around the same length of time I did. So I think he wanted to come back, but we also wanted to visit him down there before he left. We had also been wanting to go to Patagonia for ages (long before his trip was on the table), so it was perfect. 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

First project with the new 3D printer: a TOF sensor mount

I’m pretty late to hop on the 3D printing bandwagon, but I heard its siren song and couldn’t stay away much longer!

After the briefest search online and asking a few friends, I decided to go with the Monoprice Delta Mini. My main reasons were that I didn’t want to have to tinker and build much (at least to start), I wanted to be able to get decent quality, I didn’t want to spend a ton, and I don’t especially need a large print size. The MPDM matched all of this (supposedly works out of box, can do 0.05 mm layer height, 160 bucks, 4″ height by 3″ diameter print volume), so I went for it. read more

Solving the Brachistochrone and a cool parallel between diversity in genetic algorithms and simulated annealing

In my first post on Genetic Algorithms (GA), I mentioned at the end that I wanted to try doing some other applications of them, rather than just the N Queens Problem. In the next post, I built the “generic” GA algorithm structure, so it should be easy to test with other “species”, but didn’t end up using it for any applications.

I thought I’d do a bunch of applications, but the first one actually ended up being pretty interesting, so… here we are. read more

Animation stand: from design to build with Onshape

My mom does lots of animation, and one day mentioned that she would love to have an animation stand, but professional ones are either cheap and too small, or very expensive. With the holidays coming up, this seemed like a good present!

I Googled “animation stand” first, to see what others have done and get some inspiration.

Training an RL agent to play Puckworld with a DDQN

Last time I messed around with RL, I solved the classic Mountain Car problem using Q-learning and Experience Replay (ER).

However, it was very basic in a lot of ways:

  • There are really only two actions, and the state space had only two dimensions (position and velocity).
  • The way I was representing the state space was very simple, “coarse coding”, which breaks the continuous state space into discrete chunks, so in a way it still has discrete states. More interesting problems have continuous, many dimensional state spaces.
  • The representation of Q was just a state vector times a weight vector, so just linear. You can actually get a decent amount done with linear, but of course all the rage these days is in using neural networks to create the Q function.
  • The problem was very “stationary”, in the sense that the flag (where the car wanted to go) was always in the same place. Even if I had the flag move around from episode to episode, the strategy would always be the same: try to pick up enough momentum by going back and forth. A more interesting problem is one where the goal moves.