Discovering Relational Domain Features for Probabilistic Planning

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Discovering Relational Domain Features for Probabilistic Planning
In sequential decision-making problems formulated as Markov decision processes, state-value function approximation using domain features is a critical technique for scaling up the feasible problem size. We consider the problem of automatically finding useful domain features in problem domains that exhibit relational structure. Specifically we consider learning compact relational features without input from human expertise; we use neither expert decisions nor human domain knowledge beyond the basic domain definition. We propose a method to learn relational features for a linear value-function representation—numerically valued features are selected by their fit to the Bellman residual of the current value function and are automatically learned and added to the representation when needed. Starting with only a trivial feature in the value-function representation, our method finds useful value functions by combining feature learning with approximate value iteration. Empirical work p...
Jia-Hong Wu, Robert Givan
Added 02 Oct 2010
Updated 02 Oct 2010
Type Conference
Year 2007
Where AIPS
Authors Jia-Hong Wu, Robert Givan
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