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Erschienen in:
2003 | OriginalPaper | Buchkapitel
Exploring the T-Maze: Evolving Learning-Like Robot Behaviors Using CTRNNs
verfasst von : Jesper Blynel, Dario Floreano
Erschienen in: Applications of Evolutionary Computing
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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This paper explores the capabilities of continuous time recurrent neural networks (CTRNNs) to display reinforcement learning-like abilities on a set of T-Maze and double T-Maze navigation tasks, where the robot has to locate and “remember” the position of a reward-zone. The “learning” comes about without modifications of synapse strengths, but simply from internal network dynamics, as proposed by [12]. Neural controllers are evolved in simulation and in the simple case evaluated on a real robot. The evolved controllers are analyzed and the results obtained are discussed.