Approximate Planning for Factored POMDPs using Belief State Simplification
David A. McAllester, Satinder Singh
TL;DR
This paper presents a planning algorithm for factored POMDPs that leverages belief state simplification to balance accuracy and computational efficiency, building on recent theoretical advances.
Contribution
It introduces a novel planning approach for factored POMDPs that utilizes belief state simplification to improve efficiency while maintaining accuracy.
Findings
Effective belief state simplification improves planning efficiency.
The proposed method balances accuracy and computational cost.
The approach extends existing theoretical frameworks for POMDP planning.
Abstract
We are interested in the problem of planning for factored POMDPs. Building on the recent results of Kearns, Mansour and Ng, we provide a planning algorithm for factored POMDPs that exploits the accuracy-efficiency tradeoff in the belief state simplification introduced by Boyen and Koller.
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Taxonomy
TopicsMachine Learning and Algorithms · Bayesian Modeling and Causal Inference · Reinforcement Learning in Robotics