Modelling Human Player Sensorial and Actuation Limitations in Artificial Players

In game design, one of the most important tasks is associated with the playtesting process, as this is where game designers are able to understand if the game experience they are trying to create is indeed being passed to the players. How a player perceives and reacts to a game should be important in the game design process. Work developed in the Deep Learning research field has proved to be a great source of information as to understand how an agent is capable of achieving great runs and scores playing various games from scratch. The capability of testing different games using a screen capture artificial player powered by Convolutional Neural Network allows for a good understanding of how an agent is capable of extracting important features from the game screen without additional information. This Thesis, took the work developed in the Deep Reinforcement Learning field applied to Atari environments, mainly Deep-Q Networks, modulated different types of player limitations, tested and documented the results achieved. The results seem to indicate the existence of different types of playing patterns for different limitations.