dc.contributor.author | Jacob, D. | |
dc.contributor.author | Polani, D. | |
dc.contributor.author | Nehaniv, C.L. | |
dc.date.accessioned | 2011-11-22T09:01:10Z | |
dc.date.available | 2011-11-22T09:01:10Z | |
dc.date.issued | 2005 | |
dc.identifier.citation | Jacob , D , Polani , D & Nehaniv , C L 2005 , Legs that can walk: Embodiment-Based Modular Reinforcement Learning applied . in Procs 2005 IEEE Int Symposium on Computational Intelligence in Robotics and Automation : CIRA 2005 . Institute of Electrical and Electronics Engineers (IEEE) , pp. 365-372 , 2005 IEEE Int Symposium of Computational Intelligence in Robotics & Automation , Espoo , Finland , 27/06/05 . | |
dc.identifier.citation | conference | |
dc.identifier.isbn | 0-7803-9355-4 | |
dc.identifier.other | dspace: 2299/932 | |
dc.identifier.other | ORCID: /0000-0002-3233-5847/work/86098131 | |
dc.identifier.uri | http://hdl.handle.net/2299/7065 | |
dc.description.abstract | Experiments to illustrate a novel methodology for reinforcement learning in embodied physical agents are described. A simulated legged robot is decomposed into structurebased modules following the authors' EMBER principles of local sensing, action and learning. The legs are individually trained to 'walk' in isolation, and re-attached to the robot; walking is then sufficiently stable that learning in situ can continue. The experiments demonstrate the benefits of the modular decomposition: state-space factorisation leads to faster learning, in this case to the extent that an otherwise intractable problem becomes learnable. | en |
dc.format.extent | 4797286 | |
dc.language.iso | eng | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | |
dc.relation.ispartof | Procs 2005 IEEE Int Symposium on Computational Intelligence in Robotics and Automation | |
dc.title | Legs that can walk: Embodiment-Based Modular Reinforcement Learning applied | en |
dc.contributor.institution | School of Computer Science | |
dc.contributor.institution | Centre for Computer Science and Informatics Research | |
dc.contributor.institution | Department of Computer Science | |
dc.contributor.institution | Adaptive Systems | |
dc.contributor.institution | School of Physics, Engineering & Computer Science | |
dc.contributor.institution | Centre for Future Societies Research | |
dc.contributor.institution | Science & Technology Research Institute | |
rioxxterms.type | Other | |
herts.preservation.rarelyaccessed | true | |