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AAMAS
2002
Springer

Relational Reinforcement Learning for Agents in Worlds with Objects

11 years 7 months ago
Relational Reinforcement Learning for Agents in Worlds with Objects
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 interrelated 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.
Saso Dzeroski
Added 16 Dec 2010
Updated 16 Dec 2010
Type Journal
Year 2002
Where AAMAS
Authors Saso Dzeroski
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