Trabalho de Conclusão de Curso | Graduação
URI permanente desta comunidadehttps://repositorio.insper.edu.br/handle/11224/3244
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2 resultados
Resultados da Pesquisa
Trabalho de Conclusão de Curso Building NPCs for a real-time multiplayer game with Artificial Intelligence(2024) Duarte, Diogo dos Reis; Cunha, Eduardo Araujo Rodrigues da; Barbosa, Letícia Coêlho; Domingos, Lídia Alves ChagasThis project developed NPCs (Non-Playable Characters) using Reinforcement Learning for the multiplayer game Arena of Dreams, developed by the company Fanatee, which blends the genres of Party Royale and Trivia, forming a game of various mini games in pursuit of the podium. The project's purpose is to construct an artificial intelligence model that enables an agent to behave like other players, capable of performing the required activities in the game, allowing the match to start even without the minimum required number of people. For the construction of the model, the Unity game engine was used in conjunction with the Unity Machine Learning Agents Toolkit (ML-Agents), which is an artificial intelligence agent system.Trabalho de Conclusão de Curso Search of shipwrecked people using drone swarms (part 2)(2024) Oliveira, Jorás Custódio Campos de; Andrade, Pedro Henrique Britto Aragão; Falcão, Renato Laffranchi; Rodrigues, Ricardo RibeiroThe project's purpose is to iterate on the given multi-agent Drone Swarm Search Environment (DSSE) and research into Reinforcement Learning methods. The DSSE was created with the direct purpose of using reinforcement learning algorithms to train swarms of drones to execute autonomous maritime search and rescue missions of shipwrecked people in the ocean. The environment simulates the movement of persons-in-water (PIW) considering the ocean's dynamic circumstances and calculates a dynamic map of probabilities to be given to the agents, with two distinct environments, one for rescue scenarios with simulated PIW and a second expanding on state-of-the-art research for maritime coverage search path planning. The DSSE facilitates the training and visualization of drone behavior, the project emphasizes continuous improvement and open accessibility, with the release of the DSSE as an open-source Python package and documentation. The focus is on the continuous improvement of simulation quality and applicability of the environments for research purposes, with development, training and evaluation of Reinforcement learning algorithms to improve the path planning of autonomous agents, for search and rescue maritime scenarios.