Studies for a Graphics Engine with an Artificial Intelligence System

The potential of Artificial Intelligence and Deep Learning technology has been explored in various sectors through ongoing research projects. It is proposed in this chapter to use a Deep Q-Network (DQN) to train a convolutional neural network using Q-Learning. The idea is to develop an algorithm that enables a virtual character to learn through trial and error, similar to the way humans learn. The aim of the study is to achieve a more natural and effective interaction between humans and virtual entities and also to cultivate a thorough comprehension of the fundamental ideas and methodologies associated with the integration of artificial intelligence into graphics engines. This research represents a significant step towards creating a self-learning virtual character that can interact seamlessly with humans. If successful, this approach can provide a basis for automating and optimizing time-consuming tasks entities perform without a preprogrammed intellectual capacity. The authors demonstrated a learning process similar to a "match to sample task." In this process, the character must recall precisely what tasks were rewarded. As the character repeats the tasks, they seek the same events that lead to rewards. This paper presents a well-researched concept that could pave the way for automating laborious processes that entities without a preprogrammed intellectual capacity perform. The success of this research could lead to a more efficient and effective way of interaction between humans and virtual entities, ultimately enhancing the quality of life.