Exploring the T-Maze: Evolving Learning-Like Robot Behaviors using CTRNNs

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.


Published in:
Applications of Evolutionary Computing
Presented at:
2nd European Workshop on Evolutionary Robotics (EvoRob'2003), Essex, UK, 14-16 April
Year:
2003
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Note:
Raidl, G. et al. (eds.)
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 Record created 2006-01-12, last modified 2018-03-17

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