Video games, in addition to representing an extremely relevant field of entertainment and market, have been widely used as a case study in artificial intelligence for representing a problem with a high degree of complexity. In such studies, the investigation of approaches that endow player agents with the ability to retrieve relevant information from game scenes stands out, since such information can be very useful to improve their learning ability. This work proposes and analyses new deep learning-based models to identify game events occurring in Super Mario Bros gameplay footage. The architecture of each model is composed of a feature extractor convolutional neural network (CNN) and a classifier neural network (NN). The extracting CNN aims to produce a feature-based representation for game scenes and submit it to the classifier, so that the latter can identify the game event present in each scene. The models differ from each other according to the following elements: the type of the CNN; the type of the NN classifier; and the type of the game scene representation at the CNN input, being either single frames, or chunks, which are n-sequential frames (in this paper 6 frames were used per chunk) grouped into a single input. The main contribution of this article is to demonstrate the greater performance reached by the models which combines the chunk representation for the game scenes with the resources of the classifier recurrent neural networks (RNN).

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Checkers player agents represent an appropriate case study for the best unsupervised methods of Machine Learning. This work presents a tool to measure the performance of these methods based on the quality of the decision making of these agents. The proposed tool, based on the data of movements performed in real games by the agents under evaluation, provides a statistical way of automatically comparing the coincidence rates between the decision making of the evaluated agents with those that the remarkable player agent Cake would do in the same situations. The tool was validated through tournaments between agents comparing their respective coincidence rates and their performance.

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The objective behind this study is to investigate appropriate Machine Learning (ML) techniques-more speciĄcally, Deep Learning (DL)-combined with methods from Computer Vision (CV) for state representation by images, to produce agents capable of solving problems, in real time, in environments with complex properties. Such difficulties require agents to be highly efficient in their learning (and, consequently, decision-making) and environmental perception processes, without which they will not be successful. The digital game FIFA-soccer simulator-is used as a case study because it represents a realistic and challenging environment. The ML techniques are investigated in the context of the DL provided by Convolutional Neural Networks (CNNs), being: imitation learning, used here with the purpose of endowing the agent with the ability to solve problems in a way closer to human; by deep reinforcement, in which the agent is trained in an attempt to autonomously abstract an optimal decision-making policy. Regarding the environmental perception, the following state representations approaches are investigated in this study: raw images-with and without color information-and through Object Detection Techniques (ODT). In order to further improve the performance of the agents produced, genetic algorithm techniques are explored to automatically deĄne a CNN architecture to be used as the player agents decision-making module. In addition to corroborating the excellent results that DL combined with CV has been producing in the context of ML (particularly in games), the present work shows the great potential of the application of ODT in the process of echancing the environmental perception, which counts as a relevant counterpart to the fact that ODT demands computational procedures with a higher cost in relation to the representations based on raw images.