Abstract
Blynel et al. [2] recently compared two types of recurrent neural network, Continuous Time Recurrent Neural Networks (CTRNNs) and Plastic Neural Networks (PNNs), on their ability to control the behaviour of a robot in a simple learning task; they found little difference between the two. However, this may have been due to the simplicity of their task. Our comparison on a slightly more complex task yielded very different results: 70% runs with CTRNNs produced successful learning networks; runs with PNNs failed to produce a single success.
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References
R.D. Beer. Dynamical approaches to cognitive science. Trends in Cognitive Sciences, 4(3):91–99, 2000.
J. Blynel and D. Floreano. Levels of Dynamics and Adaptive Behaviour in Evolutionary Neural Controllers. In B. Hallam, D. Floreano, J. Hallam, G. Hayes, and J-A. Meyer, editors, FROM ANIMALS TO ANIMATS VII: Proceedings of the 7th international conference on the simulation of adaptive behavior (SAB’02), pages 272–281, Cambridge, MA, 4-9 August 2002. MIT Press.
D. Floreano and J. Urzelai. Evolution and learning in autonomous robotic agents. In D. Mange and M. Tomassini, editor, Bio-Inspired Computing Systems, chapter 1. PPUR, Lausanne, 1998.
D. Floreano and J. Urzelai. Evolution of plastic control networks. Autonomous Robots, 11(3):311–317, 2001.
D. Floreano and J. Urzelai. Neural Morphogenesis, Synaptic Plasticity, and Evolution. Theory in Biosciences, 120(3–4):223–238, 2001.
S. Nol. and D. Floreano. Learning and evolution. Autunomous Robots, 7(1):89–113, 1999.
E. Tuci, I. Harvey, and M. Quinn. Evolving integrated controllers for autonomous learning robots using dynamic neural networks. In B. Hallam, D. Floreano, J. Hallam, G. Hayes, and J-A. Meyer, editors, FROM ANIMALS TO ANIMATS VII: Proceedings of the 7th international conference on the simulation of adaptive behavior (SAB’02), Cambridge, MA, 4-9 August 2002. MIT press.
B. M. Yamauchi and R. D. Beer. Integrating Reactive, Sequential, and Learning Behavior Using Dynamical Neural Networks. In D. Cli., P. Husbands, J.-A. Meyer, and S. W. Wilson, editors, From Animals to Animats III: Proceedings of the 3rd International Conference on Simulation of Adaptive Behavior. MIT Press, 1994.
B. M. Yamauchi and R. D. Beer. Sequential behavior and learning in evolved dynamical neural networks. Adaptive Behavior, 2(3):219–246, 1994.
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Tuci, E., Quinn, M. (2003). Behavioural Plasticity in Autonomous Agents: A Comparison between Two Types of Controller. In: Cagnoni, S., et al. Applications of Evolutionary Computing. EvoWorkshops 2003. Lecture Notes in Computer Science, vol 2611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36605-9_60
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DOI: https://doi.org/10.1007/3-540-36605-9_60
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