Browsing by Author "Foleiss, Juliano Henrique"
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- Reinforcement Learning in Tower DefensePublication . Dias, Augusto Vicente Fernandes; Foleiss, Juliano Henrique; Lopes, Rui PedroReinforcement learning is a machine learning technique that makes a decision based on a sequence of actions. This allows changing a game agent’s behavior through feedback, such as rewards or penalties for their actions. Recent work has been demonstrating the use of reinforcement learning to train agents capable of playing electronic games and obtain scores even higher than professional human players. These intelligent agents can also assume other roles, such as creating more complex challenges to players, improving the ambiance of more complex interactive games and even testing the behavior of playerswhen the game is in development. Some literature has been using a deep learning technique to process an image of the game. This is known as the deep Q network and is used to create an intermediate representation and then process it by layers of neural network. These layers are capable of mapping game situations into actions that aim to maximize a reward over time. However, this method is not feasible in modern games, rendered in high resolution with an increasing frame rate. In addition, this method does not work for training agents who are not shown on the screen. In this work we propose a reinforcement learning pipeline based on neural networks, whose input is metadata, selected directly in the game state, and the actions are mapped directly into high-level actions by the agent.We propose this architecture for a tower defense player agent, a real time strategy game whose agent is not represented on the screen directly.
- Thermal-based nutritional recommendations: aquaVitae systemPublication . Marcuzzo, Henrique; Pereira, Maria João; Alves, Paulo; Foleiss, Juliano HenriqueIn this article, we introduce an innovative recommendation system designed to assist nutritionists in creating personalized and effective nutritional plans for their clients. The system gathers data on user preferences, dietary restrictions, and nutritional needs, providing a variety of meal options tailored to each individual. Continuous diet monitoring of patients and prioritizing thermogenic foods are additional features that enhance efficiency and user experience.