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Abstract

This paper investigates how a goal-directed autonomous mobile robot can rapidly adapt its behaviour in order to navigate efficiently in an unknown indoor environment. The reinforcement learning approach is used to improve the performance of TESEO, an autonomous mobile robot controlled by a neural network. TESEO's network architecture and action unit are explained and its four basic forms of learning are presented.

Additional information

Authors: MILLAN J DEL R, JRC Ispra (IT)
Bibliographic Reference: Paper presented: From Animals to Animats : 3rd Conference on Simulation of Adaptive Behavior, Brighton (GB), August 8-12, 1994
Availability: Available from (1) as Paper EN 38305 ORA
Record Number: 199410768 / Last updated on: 1994-11-28
Category: PUBLICATION
Original language: en
Available languages: en
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