Monte-Carlo Planning in Large POMDPs

  title={Monte-Carlo Planning in Large POMDPs},
  author={David Silver and Joel Veness},
This paper introduces a Monte-Carlo algorithm for online planning in large POMDPs. The algorithm combines a Monte-Carlo update of the agent’s belief state with a Monte-Carlo tree search from the current belief state. The new algorithm, POMCP, has two important properties. First, MonteCarlo sampling is used to break the curse of dimensionality both during belief state updates and during planning. Second, only a black box simulator of the POMDP is required, rather than explicit probability… CONTINUE READING
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