Objective We propose a new paradigm for learning in complex high-dimensions dynamic environments. Our goal is to develop algorithms, theory, and applications that use plurality of learning approaches and models in a synergetic way. Our paradigm considers the task of learning a control policy by combining trial and error in the style of reinforcement learning with learning from a competent teacher whose interaction with the environment can be observed. Instead of using the teacher for imitation, our paradigm is focused on learning good representations of the world-model. We consider four specific issues in the new paradigm: (i) The usage of iteration and reiteration between learning from a teacher and reinforcement learning. (ii) Learning representation and structure from the teacher. (iii) Optimizing policies based on learned representations and reasoning about model uncertainty. (iv) Learning sub-strategies from a teacher and when and how to use them. We will develop algorithms and theory pertaining to the new paradigm and will apply it in two challenging domains: a fighter jet simulator and a network operating center simulator. Fields of science natural sciencescomputer and information sciencesartificial intelligencemachine learningreinforcement learning Programme(s) FP7-PEOPLE - Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) Topic(s) FP7-PEOPLE-2009-RG - Marie Curie Action: "Reintegration Grants" Call for proposal FP7-PEOPLE-2009-RG See other projects for this call Funding Scheme MC-IRG - International Re-integration Grants (IRG) Coordinator TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY EU contribution € 100 000,00 Address SENATE BUILDING TECHNION CITY 32000 Haifa Israel See on map Activity type Higher or Secondary Education Establishments Administrative Contact Mark Davison (Mr.) Links Contact the organisation Opens in new window Website Opens in new window Total cost No data
