Reinforcement learning in videogames

While there are still a lot of projects and papers focused on: given a game, discover and measure which is the best algorithm for it, I decided to twist things around and decided to focus on two algorithms and its parameters be able to tell which games will be best approachable with it. To do this, I will be implementing both algorithms Q-Learning and SARSA, helping myself with Neural Networks to be able to represent the vast state space that the games have. The idea is to implement the algorithms as general as possible.This way in case someone wanted to use my algorithms for their game, it would take the less amount of time possible to adapt the game for the algorithm. I will be using some games that are used to make Artificial Intelligence competitions so I have a base to work with, having more time to focus on the actual algorithm implementation and results comparison.