{"paper":{"title":"Speeding Up the Convergence of Value Iteration in Partially Observable Markov Decision Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"N. L. Zhang, W. Zhang","submitted_at":"2011-06-01T16:40:25Z","abstract_excerpt":"Partially observable Markov decision processes (POMDPs) have    recently become popular among many AI researchers because they serve    as a natural model for planning under uncertainty.  Value iteration is    a well-known algorithm for finding optimal policies for POMDPs.  It    typically takes a large number of iterations to converge.  This paper    proposes a method for accelerating the convergence of value iteration.    The method has been evaluated on an array of benchmark problems and    was found to be very effective: It enabled value iteration to converge    after only a few iterations"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1106.0251","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}