In contrast to previous competitions, where the problems were goal-based, the 2011 International Probabilistic Planning Competition (IPPC-2011) emphasized finite-horizon reward maximization problems with large branching factors. These MDPs modeled more realistic planning scenarios and presented challenges to the previous state-of-the-art planners (e.g., those from IPPC-2008), which were primarily based on domain determinization – a technique more suited to goal-oriented MDPs with small branching factors. Moreover, large branching factors render the existing implementations of RTDP- and LAO*-style algorithms inefficient as well. In this paper we present GLUTTON, our planner at IPPC-2011 that performed well on these challenging MDPs. The main algorithm used by GLUTTON is LR 2TDP, an LRTDP-based optimal algorithm for finite-horizon problems centered around the novel idea of reverse iterative deepening. We detail LR 2TDP itself as well as a series of optimizations included in GLUTTON that help LR 2TDP achieve competitive performance on difficult problems with large branching factors – subsampling the transition function, separating out natural dynamics, caching transition function samples, and others. Experiments show that GLUTTON and PROST, the IPPC-2011 winner, have complementary strengths, with GLUTTON demonstrating superior performance on problems with few high-reward terminal states.<\/p>\n","protected":false},"excerpt":{"rendered":"
In contrast to previous competitions, where the problems were goal-based, the 2011 International Probabilistic Planning Competition (IPPC-2011) emphasized finite-horizon reward maximization problems with large branching factors. These MDPs modeled more realistic planning scenarios and presented challenges to the previous state-of-the-art planners (e.g., those from IPPC-2008), which were primarily based on domain determinization – a technique […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13561],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-340955","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-algorithms","msr-locale-en_us"],"msr_publishername":"AAAI Press","msr_edition":"","msr_affiliation":"","msr_published_date":"2012-6-25","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"340958","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/12\/GLUTTON.pdf","id":"340958","title":"glutton","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"user_nicename","value":"Andrey Kolobov","user_id":30910,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Andrey Kolobov"},{"type":"text","value":"Peng Dai","user_id":0,"rest_url":false},{"type":"text","value":"Mausam","user_id":0,"rest_url":false},{"type":"text","value":"Daniel S. 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