Abstract
This paper presents an introduction to reinforcement learning and relational reinforcement learning at a level to be understood by students and researchers with different backgrounds.
It gives an overview of the fundamental principles and techniques of reinforcement learning without involving a rigorous deduction of the mathematics involved through the use of an example application.
Then, relational reinforcement learning is presented as a combination of reinforcement learning with relational learning. Its advantages - such as the possibility of using structural representations, making abstraction from specific goals pursued and exploiting the results of previous learning phases - are discussed.
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© 2001 Springer-Verlag Berlin Heidelberg
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Driessens, K. (2001). Relational Reinforcement Learning. In: Luck, M., Mařík, V., Štěpánková, O., Trappl, R. (eds) Multi-Agent Systems and Applications. ACAI 2001. Lecture Notes in Computer Science(), vol 2086. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47745-4_12
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DOI: https://doi.org/10.1007/3-540-47745-4_12
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