Neural Network

Introduction In my previous post I made a neural network from scratch with the idea of using it for a tic-tac-toe agent. In this post I’ll go over the training process. My general intuition is to play each agent with all other agents twice, once for “o” and once for “x”. A reward function would decide what score an agent gets each game. The agents with top score progress to the next epoch, where they are cloned and mutated to fill the dropped out population. ...

Introduction In my last post, I used a minimax algorithm to create a tic-tac-toe agent that sees a few steps ahead and can’t be beat. While this approach works for simple games like this one, it wouldn’t scale well for more complex games. For this reason, I wanted to implement an agent that can use a neural network to find patterns in the game through machine learning. If I can make this for tic-tac-toe, the general approach should be usable for other games. ...