## Beating OpenAI games with neuroevolution agents: pretty NEAT!

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.

## 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:

## Genetic Algorithms, part 2

Last time, in case you missed it, I left off with a laundry list of things I wanted to expand on with Genetic Algorithms (GA). Let’s see which of those I can do this time!

This is pretty wordy and kind of dry, since I was just messing around and figuring stuff out, but I promise the next one will have some cool visuals.

## Mountain car, Q-learning, and Experience Replay with Pytorch

Hey there!

Mountain Car (MC) is a classic Reinforcement Learning (RL) problem. It was briefly shown in a video I was watching, so I figured I’d give it a shot.

## Using Reinforcement Learning to solve the Egg drop puzzle

So last time, I solved the egg drop puzzle in a few ways. One of them was using a recent learn, Markov Decision Processes (MDP). It worked, which got me really stoked about them, because it was such a cool new method to me.

However, it’s kind of a baby process that’s mostly used as a basis to learn about more advanced techniques. In that solution to the problem, I defined the reward matrix and the transition probability matrix , and then used them explicitly to iteratively solve for the value function v and the policy p. This works, but isn’t very useful for the real world, because in practice you don’t know  and , you just get to try stuff and learn the best strategy through experience. So the real challenge would be letting my program try a bunch of actual egg drops, and have it learn the value function and policy from them.

## Fun with Genetic Algorithms and the N Queens Problem

Genetic Algorithms are cool!

I was recently skimming through Russel and Norvig’s AI: A Modern Approach and came to the section on Genetic Algorithms (GA). Briefly, they’re a type of algorithm inspired by genetics and evolution, in which you have a problem you’d like to solve and some initial attempts at solutions to the problem, and you combine those solutions (and randomly alter them slightly) to hopefully produce better solutions. It’s cool for several reasons, but one really cool one is that they’re often used to “evolve” to an optimal solution in things like design of objects (see the antenna in the Wikipedia article). So, that’s kind of doing evolution on objects rather than living things. Just take a look at the applications they’re used for.

## EDX Artificial Intelligence, week 4

Week 4 is where it gets really good. Week 3 was cool because it got into heuristic search, which is the start of what feels like a glimmer of “intelligence”, but week 4 is on adversarial search and games. Hot damn that’s cool. Additionally (skip to the bottom if you’re only interested in that), the project for the week was to make an AI that plays the game 2048!

Theory

## EDX Artificial Intelligence, weeks 2 and 3

Hiya!

I started this AI course with my friends a while ago, but we never ended up finishing it. I’m interested in AI these days, so I thought I’d try it on my own. Week 1 is some fluff that’s not worth going over. I’m doing weeks 2 and 3 together because there is one project for both combined. The first couple sections are just notes I took on the videos and concepts. The stuff for the project is at the bottom.