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Author Caligiore, Daniele ♦ Mirolli, Marco ♦ Parisi, Domenico ♦ Baldassarre, Gianluca
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Description Organisms, and especially primates, are able to learn several skills while avoiding catastrophic interference and enhancing gen-eralisation. This paper proposes a novel re-inforcement learning (RL) architecture which has a number of features that make it suit-able to investigate these phenomena. The model instantiates a mixture of expert archi-tecture within a neural-network actor-critic system trained with the TD(λ) RL algorithm. The “responsibility signals ” provided by the gating network are used both to weight the outputs of the multiple “expert ” controllers and to modulate their learning. The model is tested in a simulated dynamic 2D robotic arm which autonomously learns to reach a target in (up to) three different conditions. The re-sults show that the model is able to train same or different experts to solve the task(s) in the various conditions depending on the similarity of the sensorimotor mappings they require. 1.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Institution Proceedings of the Tenth International Conference on Epigenetic Robotics. (2010