Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes
Most exact algorithms for general partially observable Markov decision processes (POMDPs) use a form of dynamic programming in which a piecewise-linear and convex representation of one value function is transformed into another. We examine variations of the "incremental pruning" method for solving this problem and compare them to earlier algorithms from theoretical and empirical perspectives. We find that incremental pruning is presently the most efficient exact method for solving POMDPs.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Free Lunch in the Forest: Functionally-Identical Pruning of Boosted Tree Ensembles
Tree ensembles, including boosting methods, are highly effective and widely used for tabular data. However, large ensembles lack interpretability and require longer inference times. We introduce a method to prune a tree …
Incremental Fast Subclass Discriminant Analysis
This paper proposes an incremental solution to Fast Subclass Discriminant Analysis (fastSDA). We present an exact and an approximate linear solution, along with an approximate kernelized variant. Extensive experiments on…
Geometric-Based Pruning Rules For Change Point Detection in Multiple Independent Time Series
We consider the problem of detecting multiple changes in multiple independent time series. The search for the best segmentation can be expressed as a minimization problem over a given cost function. We focus on dynamic p…
Change Point DetectionComputational EfficiencyTime SeriesScout-Assisted Planning for Heterogeneous Robot Teams under Partially Known Environments
Autonomous robot teams navigating partially known environments face costly backtracking when ground robots encounter blocked roads that are only revealed upon physical traversal. We address this with Scout-Assisted Plann…
Graph Neural NetworkSlimGPT: Layer-wise Structured Pruning for Large Language Models
Large language models (LLMs) have garnered significant attention for their remarkable capabilities across various domains, whose vast parameter scales present challenges for practical deployment. Structured pruning is an…