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One of the most prominent research goals in the field of mobile autonomous robots is to create robots that are able to adapt to new environments, i.e., the robots should be able to learn during their "lifetime" possibly without (or a minimum) of human intervention. When employing artificial neural networks (ANNs) to control the robot, reinforcement learning (RL) techniques are a good candidate for achieving continuous on-line learning. A problem with RL applied to robot learning is that thedoi:10.1109/icsmc.2004.1401110 dblp:conf/smc/Mayer04 fatcat:l3qqsmllv5gudej74nsns6qd3q