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":"","msr-author-ordering":[{"type":"user_nicename","value":"Andrey Kolobov","user_id":"30910"},{"type":"text","value":"Peng Dai","user_id":0},{"type":"text","value":"Mausam","user_id":0},{"type":"text","value":"Daniel S. 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