Rollout Heuristics for Online Stochastic Contingent Planning

Oded Blumenthal, Guy Shani

Research output: Contribution to journalConference articlepeer-review


Partially observable Markov decision processes (POMDP) are a useful model for decision-making under partial observability and stochastic actions. Partially Observable Monte-Carlo Planning is an online algorithm for deciding on the next action to perform, using a Monte-Carlo tree search approach, based on the UCT (UCB applied to trees) algorithm for fully observable Markov-decision processes. POMCP develops an action-observation tree, and at the leaves, uses a rollout policy to provide a value estimate for the leaf. As such, POMCP is highly dependent on the rollout policy to compute good estimates, and hence identify good actions. Thus, many practitioners who use POMCP are required to create strong, domain-specific heuristics. In this paper, we model POMDPs as stochastic contingent planning problems. This allows us to leverage domain-independent heuristics that were developed in the planning community. We suggest two heuristics, the first is based on the well-known hadd heuristic from classical planning, and the second is computed in belief space, taking the value of information into account.

Original languageEnglish
Pages (from-to)89-101
Number of pages13
JournalElectronic Proceedings in Theoretical Computer Science, EPTCS
StatePublished - 30 Sep 2023
Event3rd Workshop on Agents and Robots for Reliable Engineered Autonomy, AREA 2023 - Krakow, Poland
Duration: 1 Oct 2023 → …

ASJC Scopus subject areas

  • Software


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