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Robot Coverage Control by Neuromodulation

Harrington, Kyle and Awa, Emmanuel and Cussat-Blanc, Sylvain and Pollack, Jordan Robot Coverage Control by Neuromodulation. (2013) In: International Joint Conference on Neural Networks - IJCNN 2013, 4 August 2013 - 9 August 2013 (Dallas, United States).

(Document in English)

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Official URL: http://dx.doi.org/10.1109/IJCNN.2013.6706784


An important connection between evolution and learning was made over a century ago and is now termed as the Baldwin effect. Learning acts as a guide for an evolutionary search process. In this study reinforcement learning agents are trained to solve the robot coverage control problem. These agents are improved by evolving neuromodulatory gene regula- tory networks (GRN) that influence the learning and memory of agents. Agents trained by these neuromodulatory GRNs can consistently generalize better than agents trained with fixed parameter settings. This work introduces evolutionary GRN models into the context of neuromodulation and illustrates some of the benefits that stem from neuromodulatory GRNs.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to IEEE editor. The definitive version is available at http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6706784&tag=1
HAL Id:hal-01147304
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Other partners > Brandeis University (USA)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Laboratory name:
Deposited On:14 Apr 2015 13:28

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