Optimal and Approximate Stochastic Planning using Decision Diagrams

  title={Optimal and Approximate Stochastic Planning using Decision Diagrams},
  author={Jesse Hoey and Robert St-Aubin and Alan Hu and Craig Boutilier},
Structured methods for solving factored Markov decision processes (MDPs) with large state spaces have been proposed recently to allow dynamic programming to be applied without the need for complete state enumeration. We present algebraic decision diagrams (ADDs) as efficient data structures for solving very large MDPs. We describe a new value iteration algorithm for exact dynamic programming, using an ADD input representation of the MDP. We extend this algorithm with an approximate version for… CONTINUE READING
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