ARTIFICIAL INTELLIGENCE AND REINFORCEMENT LEARNING

Abstract

This paper addresses artificial intelligence (AI) and reinforcement learning, exploring their fundamental concepts. A brief history of AI is presented, from its origins to recent advancements. The basic concepts of AI are discussed, such as artificial neurons, multilayer neural networks, and their application in different models like Perceptron, MLP, CNN, RNN, LSTM, Autoencoder, and GAN. The importance of information processing and the ability to make decisions in unforeseen situations, which are fundamental characteristics of AI, are highlighted. The application of reinforcement learning is also explored, an approach that aims to teach an agent to take actions in an environment to maximize a reward. Through a reading workshop, participants experienced concepts such as the chessboard challenge, illustrating the importance of weights, values, and optimization in problem-solving using AI. The workshop sparked the participants' interest, encouraging them to seek further knowledge and explore the practical applications of AI in different contexts. This paper provides a practical and engaging introduction to the concepts of AI and reinforcement learning, connecting readers to the foundations of these fields and inspiring them to contribute to innovative solutions.