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About

Thomas Trappenberg is a leading researcher in computational neuroscience and cognitive robotics, whose work bridges the gap between neural computation and autonomous decision-making. His primary research areas include reinforcement learning, neural field models, and the arbitration between habitual and planning-based control systems in artificial agents. Trappenberg’s major contributions lie in developing biologically inspired models that explain how humans and robots integrate multiple decision-making systems—such as his novel arbitration model for balancing habitual and deliberate planning, which has garnered significant attention (8+ citations). His influential textbook on reinforcement learning (19 citations) serves as a foundational resource for students and researchers alike. Trappenberg has also advanced robotics through innovative controllers using dynamic neural fields and topographical maps, enabling more adaptive and efficient robot navigation. With over 50 citations across his most-cited works, his research has shaped understanding of how internal models and reward-based learning can be harmonized for complex, multi-step tasks. His work is essential reading for anyone interested in the intersection of cognitive science, neural networks, and autonomous systems.