Dynamic programming for structured continuous Markov decision problems

  • Authors:

  • Zhengzhu Feng;Richard Dearden;Nicolas Meuleau;Richard Washington

  • Affiliations:

  • University of Massachusetts, Amherst, MA;NASA Ames Research Center, Moffet Field, CA;NASA Ames Research Center, Moffet Field, CA;NASA Ames Research Center, Moffet Field, CA

  • Venue:
  • UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
  • Year:
  • 2004

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Abstract

We describe an approach for exploiting structure in Markov Decision Processes with continuous state variables. At each step of the dynamic programming, the state space is dynamically partitioned into regions where the value function is the same throughout the region. We first describe the algorithm for piecewise constant representations. We then extend it to piecewise linear representations, using techniques from POMDPs to represent and reason about linear surfaces efficiently. We show that for complex, structured problems, our approach exploits the natural structure so that optimal solutions can be computed efficiently.