Objective
The field of machine learning develops learning paradigms and algorithms which allow systems to learn some desired functionality on their own. Supervised learning is learning with a teacher; some authoritative source provides a finite set of correct examples, and the learner generalises from the examples and learns a correct function over the entire spectrum. An example from human learning would be the learning of correct spelling by observing correctly spelled words. Reinforcement learning, on the other hand, is learning by trial and error; there is no teacher and the learner interacts directly with its environment to acquire information. The learner makes decisions arbitrarily and occasionally receives a numerical score (reinforcement signal) for its overall behaviour. This score does not indicate correct or incorrect actions, but can be used to reinforce good decision-making and discourage bad decision-making.
An example from human learning would be the process of learning how to balance and ride a bicycle (falls incur negative scores). These two fields have been researched mostly independently. Recent advances in supervised learning have demonstrated outstanding, near optimal, generalisation performance. Reinforcement learning has not reached the same level of applicability to real-world problems. This research proposal investigates the potential of using supervised learning technology for advancing reinforcement learning. It is possible to incorporate supervised learning algorithms within the inner loops of several reinforcement learning algorithms and therefore reduce one problem to the other. This synergy opens the door to a variety of promising combinations. The proposed research will establish the criteria under which this reduction is possible, will investigate viable combinations, will propose novel algorithms, will assess their potential, and will apply them to real problems of practical interest to demonstrate their effectiveness.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- social sciences educational sciences didactics
- natural sciences computer and information sciences artificial intelligence machine learning supervised learning
- natural sciences computer and information sciences software
- natural sciences computer and information sciences artificial intelligence machine learning reinforcement learning
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering robotics
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
FP6-2004-MOBILITY-12
See other projects for this call
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
IRG - Marie Curie actions-International re-integration grants
Coordinator
CHANIA
Greece
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.