Título: Multi-Agent Reinforcement Learning with Multiple Sensory Systems

Data: 07/08/2023

Horário: 15h30

Local: Videoconferência

Resumo:

Recent advances in artificial intelligence have enabled the development of intelligent agents that can learn to perform complex tasks through reinforcement learning. These agents can operate in a wide range of environments, using various sensory modalities to perceive and interact with the world. However, many real-world problems require the coordination and cooperation of multiple agents, which presents a unique set of challenges for deep reinforcement learning (DRL). In particular, multi-agent settings require agents to learn to optimize their individual objectives and coordinate with each other to achieve a common goal.

Banca examinadora:

• Prof. Dr. Joaquim Bento Cavalcante Neto (MDCC/UFC - Orientador)
• Prof. Dr. Yuri Lenon Barbosa Nogueira (UFC - Coorientador)
• Prof. Dr. Creto Augusto Vidal (UFC)