Definition
Relational reinforcement learning is concerned with learning behavior or control policies based on a numerical feedback signal, much like standard reinforcement learning, in complex domains where states (and actions) are largely characterized by the presence of objects, their properties, and the existing relations between those objects. Relational reinforcement learning uses approaches similar to those used for standard reinforcement learning, but extends these with methods that can abstract over specific object identities and exploit the structural information available in the environment.
Motivation and Background
Reinforcement learningis a very attractive machine learning framework, as it tackles, in a sense, the whole artificial intelligence problem at a small scale: an agent acts in an unknown environment and has to learn how to behave optimally by reinforcement, i.e., through...
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Driessens, K. (2017). Relational Reinforcement Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_726
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