Q-learning with Neural Networks

We've made it to what we've all been waiting for, Q-learning with neural networks. Since I'm sure a lot of people didn't follow parts 1 and 2 because they were kind of boring, I will attempt to make this post relatively (but not completely) self-contained. In this post, we will dive into using Q-learning to train an agent (player) how to play Gridworld. Gridworld is a simple text based game in which there is a 4x4 grid of tiles and 4 objects placed therein: a player, pit, goal, and a wall. The player can move up/down/left/right ( a \in A \{up,down,left,right\}) and the point of the game is to get to the goal where the player will receive a numerical reward. Unfortunately, we have to avoid a pit, because if we land on the pit we are penalized with a negative'reward'.