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AIPS
2004

Heuristic Refinements of Approximate Linear Programming for Factored Continuous-State Markov Decision Processes

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Heuristic Refinements of Approximate Linear Programming for Factored Continuous-State Markov Decision Processes
Approximate linear programming (ALP) offers a promising framework for solving large factored Markov decision processes (MDPs) with both discrete and continuous states. Successful application of the approach depends on the choice of an appropriate set of feature functions defining the value function, and efficient methods for generating constraints that determine the convex space of the solution. The application of the ALP in continuous state-space settings poses an additional challenge
Branislav Kveton, Milos Hauskrecht
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Where AIPS
Authors Branislav Kveton, Milos Hauskrecht
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