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APRICODD: Approximate Policy Construction Using Decision Diagrams

Robert St‐Aubin, Jesse Hoey, Craig E. Boutilier

Open publisher page 120 citations

Abstract

We propose a method of approximate dynamic programming for Markov decision processes (MDPs) using algebraic decision diagrams (ADDs). We produce near-optimal value functions and policies with much lower time and space requirements than exact dynamic programming. Our method reduces the sizes of the intermediate value functions generated during value iteration by replacing the values at the terminals of the ADD with ranges of values. Our method is demonstrated on a class of large MDPS (with up to 2 billion states), and we compare the results with the optimal value functions.

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What this paper is about

We propose a method of approximate dynamic programming for Markov decision processes (MDPs) using algebraic decision diagrams (ADDs). We produce near-optimal value functions and policies with much lower time and space requirements than exact dynamic programming. Our method reduces the sizes of the intermediate value functions generated during value iteration by replacing the values at the terminals of the ADD with ranges of values. Our method is demonstrated on a class of large MDPS (with up to 2 billion states), and we compare the results with the optimal value functions.

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OpenAlex reports 120 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

We propose a method of approximate dynamic programming for Markov decision processes (MDPs) using algebraic decision diagrams (ADDs). We produce near-optimal value functions and policies with much lower time and space requirements than exact dynamic programming. Our method reduces the sizes of the intermediate value functions generated during value iteration by replacing the values at the terminals of the ADD with ranges of values. Our method is demonstrated on a class of large MDPS (with up to 2 billion states), and we compare the results with the optimal value functions.

Key concepts: Markov decision process, Dynamic programming, Influence diagram, Computer science, Mathematical optimization, Value (mathematics), Bellman equation, Class (philosophy)

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