Evolving Reinforcement Learning-Like Abilities for Robots

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Evolving Reinforcement Learning-Like Abilities for Robots
Abstract. In [8] Yamauchi and Beer explored the abilities of continuous time recurrent neural networks (CTRNNs) to display reinforcementlearning like abilities. The investigated tasks were generation and learning of short bit sequences. This “learning” came about without modifications of synaptic strengths, but simply from internal dynamics of the evolved networks. In this paper this approach will be extended to two embodied agent tasks, where simulated robots have acquire and retain “knowledge” while moving around different mazes. The evolved controllers are analyzed and the results are discussed.
Jesper Blynel
Added 06 Jul 2010
Updated 06 Jul 2010
Type Conference
Year 2003
Where ICES
Authors Jesper Blynel
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