Avaliação da competição no treino de agentes autônomos com Aprendizado Profundo por Reforço em jogos de Tiro em Primeira Pessoa

• Ano de defesa: 2018
• Autor(a) principal: Serafim, Paulo Bruno de Sousa
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: Agentes autônomos; Aprendizado por reforço; Deep Learning; Aprendizado competitivo; Jogos de tiro em primeira pessoa
• Link de acesso: http://www.repositorio.ufc.br/handle/riufc/33099

This work evaluates competition in training of autonomous agents immersed in First-Person Shooter games using Deep Reinforcement Learning. The agents are composed of a Deep Neural Network, which is trained using Deep Q-Learning. The input of the networks is only the pixels of the screen, allowing the creation of general players, capable of handling several environments without the need for further modifications. ViZDoom, an Application Programming Interface based on the game Doom, is used as the testbed because of its appropriate features. Fifteen agents were divided into three groups, two of which were trained by competing with each other, and the third was trained by competing against opponents that act randomly. The developed agents were able to learn adequate behaviors to survive in a custom one-on-one scenario. The tests showed that the competitive training of autonomous agents leads to a greater number of wins compared to training against non-intelligent agents.