Abstract
In reinforcement learning, an agent tries to learn a policy, i.e., how to select an action in a given state of the environment, so that it maximizes the total amount of reward it receives when interacting with the environment. We argue that a relational representation of states is natural and useful when the environment is complex and involves many inter-related objects. Relational reinforcement learning works on such relational representations and can be used to approach problems that are currently out of reach for classical reinforcement learning approaches. This chapter introduces relational reinforcement learning and gives an overview of techniques, applications and recent developments in this area.
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Džeroski, S. (2003). Relational Reinforcement Learning for Agents in Worlds with Objects. In: Alonso, E., Kudenko, D., Kazakov, D. (eds) Adaptive Agents and Multi-Agent Systems. AAMAS AAMAS 2002 2001. Lecture Notes in Computer Science(), vol 2636. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44826-8_18
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DOI: https://doi.org/10.1007/3-540-44826-8_18
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