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Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes
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Most exact algorithms for general partially observable Markov decision processes (POMDPs) use a form of dynamic programming in which a piecewise-linear and convex representation of one value function is transformed into another. We examine variations of the "incremental pruning" method for solving this problem and compare them to earlier algorithms from theoretical and empirical perspectives. We find that incremental pruning is presently the most efficient exact method for solving POMDPs.
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Cited by 2 Pith papers
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BIOMAP achieves the optimal reward on the Mask Cliff Walking benchmark by reconstructing the state graph from action vectors, but this hinges on an unstated assumption that states are geometric positions.
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