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Solving Relational and First-Order Logical Markov Decision Processes: A Survey

  • Chapter
Reinforcement Learning

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 12))

Abstract

In this chapter we survey representations and techniques for Markov decision processes, reinforcement learning, and dynamic programming in worlds explicitly modeled in terms of objects and relations. Such relational worlds can be found everywhere in planning domains, games, real-world indoor scenes and many more. Relational representations allow for expressive and natural datastructures that capture the objects and relations in an explicit way, enabling generalization over objects and relations, but also over similar problems which differ in the number of objects. The field was recently surveyed completely in (van Otterlo, 2009b), and here we describe a large portion of the main approaches. We discuss model-free – both value-based and policy-based – and model-based dynamic programming techniques. Several other aspects will be covered, such as models and hierarchies, and we end with several recent efforts and future directions.

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    Martijn van Otterlo

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    Marco Wiering

  2. , Artificial Intelligence, Radboud University Nijmegen, B.02.30 Spinozagebouw, Montessorilaan 3, Nijmegen, 6500, Netherlands

    Martijn van Otterlo

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van Otterlo, M. (2012). Solving Relational and First-Order Logical Markov Decision Processes: A Survey. In: Wiering, M., van Otterlo, M. (eds) Reinforcement Learning. Adaptation, Learning, and Optimization, vol 12. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27645-3_8

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