Papers
7
Total Citations
373
H-Index
6
About
Thomas Gabel is a leading researcher in the intersection of reinforcement learning and multi-agent systems, with a primary focus on robotic soccer simulation. His most influential work, "Reinforcement learning for robot soccer" (2009), has garnered 266 citations, establishing foundational techniques for applying machine learning to competitive, real-time environments. Gabel’s major contributions include pioneering the use of case-based reasoning (CBR) for state value function approximation in reinforcement learning, as demonstrated in his 2005 paper (37 citations), which enables agents to leverage past experiences for more efficient decision-making. He also developed the NeuroHassle approach (2009, 35 citations), a case study that significantly improved defensive behaviors in the RoboCup 2D simulation league. His earlier work on selecting heterogeneous team players through CBR (2002, 13 citations) laid groundwork for adaptive team coordination. Gabel’s research, spanning from foundational CBR methods to advanced deep learning for opponent eavesdropping (2017), has consistently pushed boundaries in autonomous agent learning. His work remains essential reading for students and researchers in robotics, multi-agent systems, and applied reinforcement learning.
Research Focus
Key Achievements
Top Papers
- 1Reinforcement learning for robot soccer266 citations · 2009
- 2CBR for State Value Function Approximation in Reinforcement Learning37 citations · 2005
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- 5Learning a Partial Behavior for a Competitive Robotic Soccer Agent11 citations · 2006
- 6Die Brainstormers: Entwurfsprinzipien lernfähiger autonomer Roboter8 citations · 2006
- 7Eavesdropping Opponent Agent Communication Using Deep Learning3 citations · 2017