Mark Edgington - researchr alias

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Viewing Publication 1 - 15 from 15

2016

Learning and Using Multimodal Stochastic Models: A Unified ApproachMark Edgington. PhD thesis, University of Bremen, Germany, 2016. [doi]

2015

Robotics echolocation test platformXiaodong Wu, Palmer D'Orazio, Mark Edgington, Miguel Abrahantes. eit 2015: 558-562 [doi]

2009

Dynamic motion modelling for legged robotsMark Edgington, Yohannes Kassahun, Frank Kirchner. iros 2009: 4688-4694 [doi]

Learning complex robot control using evolutionary behavior based systemsYohannes Kassahun, Jakob Schwendner, Jose de Gea, Mark Edgington, Frank Kirchner. gecco 2009: 129-136 [doi]

2008

EANT+KALMAN: An Efficient Reinforcement Learning Method for Continuous State Partially Observable DomainsYohannes Kassahun, Jose de Gea, Jan Hendrik Metzen, Mark Edgington, Frank Kirchner. ki 2008: 241-248 [doi]

Accelerating neuroevolutionary methods using a Kalman filterYohannes Kassahun, Jose de Gea, Mark Edgington, Jan Hendrik Metzen, Frank Kirchner. gecco 2008: 1397-1404 [doi]

Evolving Neural Networks for Online Reinforcement LearningJan Hendrik Metzen, Mark Edgington, Yohannes Kassahun, Frank Kirchner. ppsn 2008: 518-527 [doi]

Analysis of an evolutionary reinforcement learning method in a multiagent domainJan Hendrik Metzen, Mark Edgington, Yohannes Kassahun, Frank Kirchner. atal 2008: 291-298 [doi]

Towards efficient online reinforcement learning using neuroevolutionJan Hendrik Metzen, Frank Kirchner, Mark Edgington, Yohannes Kassahun. gecco 2008: 1425-1426 [doi]

Learning Walking Patterns for Kinematically Complex Robots Using Evolution StrategiesMalte Römmermann, Mark Edgington, Jan Hendrik Metzen, Jose de Gea, Yohannes Kassahun, Frank Kirchner. ppsn 2008: 1091-1100 [doi]

2007

2006

Towards a Multimodal Sensorimotor Coordination Based Object Recognition SystemYohannes Kassahun, Mark Edgington, Jose de Gea, Frank Kirchner. robio 2006: 561-566 [doi]

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