Telma Woerle de Lima
Papers
1
Total Citations
21
H-Index
1
About
Telma Woerle de Lima is a leading researcher in multiagent reinforcement learning (MARL), with a focus on bridging the gap between low-level robotic control and high-level strategic decision-making. Her most-cited work, "Multiagent Reinforcement Learning for Strategic Decision Making and Control in Robotic Soccer Through Self-Play" (2022, 21 citations), introduces a novel framework that unifies these traditionally separate domains using self-play. By enabling agents to simultaneously learn motor skills and tactical coordination, her research demonstrates how RL can produce cohesive, adaptive behaviors in complex, real-time environments like robotic soccer. This contribution is particularly impactful for advancing autonomous systems that require both physical dexterity and team-based reasoning. Lima’s work stands out for its practical integration of control theory and game-theoretic strategy, offering a scalable approach to training multiagent systems without human intervention. Her findings have implications beyond sports robotics, extending to autonomous driving, drone swarms, and industrial automation. As a rising voice in the field, Lima’s research continues to inspire new directions in embodied AI and cooperative multiagent learning.
Research Focus
Key Achievements
Top Papers
- 1