Levels of Dynamics and Adaptive Behavior in Evolutionary Neural Controllers

Two classes of dynamical recurrent neural networks, Continuous Time Recurrent Neural Networks (CTRNNs) (Yamauchi and Beer, 1994) and Plastic Neural Networks (PNNs) (Floreano and Urzelai, 2000) are compared on two behavioral tasks aimed at exploring their capabilities to display reinforcement-learning like behaviors and adaptation to unpredictable environmental changes. The networks report similar performances on both tasks, but PNNs display significantly better performance when sensory-motor re-adaptation is required after the evolutionary process. These results are discussed in the context of behavioral, biological, and computational definitions of learning.


Published in:
From Animals to Animats 7
Presented at:
7th International Conference on Simulation on Adaptive Behavior (SAB'2002), Edinburgh, UK, 4 - 9 - 11 August
Year:
2002
Keywords:
Note:
In B. Hallam, D. Floreano, J. Hallam, G. Hayes, and J.-A. Meyer (eds)
Laboratories:




 Record created 2006-01-12, last modified 2018-03-17

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