Approximate Planning for Factored POMDPs using Belief State Simplification

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.

Appears in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI1999)

References in corpus (1)

• Tractable Inference for Complex Stochastic Processes

Cited by in corpus (4)

• Value-Function Approximations for Partially Observable Markov Decision Processes
• Model-Based Bayesian Reinforcement Learning in Large Structured Domains
• Fast Planning in Stochastic Games
• Value-Directed Belief State Approximation for POMDPs