Andressa C. da Silva
Papers
1
Total Citations
5
H-Index
1
About
Andressa C. da Silva is a rising researcher in autonomous systems and artificial intelligence, with a primary focus on deep reinforcement learning for unmanned aerial vehicles (UAVs). Her most-cited work, “Parallel Distributional Prioritized Deep Reinforcement Learning for Unmanned Aerial Vehicles” (2023), introduces a novel approach to mapless UAV navigation by developing a distributed and distributional variant of the Soft Actor-Critic method, named PDSAC. This contribution addresses critical challenges in real-time decision-making for aerial robotics, enabling more efficient and robust navigation without pre-existing maps. Although early in her career, with her top paper already garnering 5 citations, da Silva’s work demonstrates significant potential for advancing autonomous drone operations in complex, dynamic environments. Her research bridges theoretical advances in reinforcement learning with practical applications in robotics, making her a promising voice in the field. As she continues to publish, her contributions are poised to influence both academic research and industrial applications in autonomous aerial systems.
Research Focus
Key Achievements
Top Papers
- 1