Self-organizing Neural Architecture for Reinforcement Learning

  • Ah-Hwee Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


Self-organizing neural networks are typically associated with unsupervised learning. This paper presents a self-organizing neural architecture, known as TD-FALCON, that learns cognitive codes across multi-modal pattern spaces, involving states, actions, and rewards, and is capable of adapting and functioning in a dynamic environment with external evaluative feedback signals. We present a case study of TD-FALCON on a mine avoidance and navigation cognitive task, and illustrate its performance by comparing with a state-of-the-art reinforcement learning approach based on gradient descent backpropagation algorithm.


Reinforcement Learn Choice Function Autonomous Vehicle Adaptive Resonance Theory Greedy Policy 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ah-Hwee Tan
    • 1
  1. 1.Intelligent Systems Centre and School of Computer EngineeringNanyang Technological UniversitySingapore

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