Sparse Tree Search Optimality Guarantees in POMDPs with Continuous Observation Spaces
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Main track. Pages 4135-4142. https://doi.org/10.24963/ijcai.2020/572
Partially observable Markov decision processes (POMDPs) with continuous state and observation spaces have powerful flexibility for representing real-world decision and control problems but are notoriously difficult to solve. Recent online sampling-based algorithms that use observation likelihood weighting have shown unprecedented effectiveness in domains with continuous observation spaces. However there has been no formal theoretical justification for this technique. This work offers such a justification, proving that a simplified algorithm, partially observable weighted sparse sampling (POWSS), will estimate Q-values accurately with high probability and can be made to perform arbitrarily near the optimal solution by increasing computational power.
Planning and Scheduling: POMDPs
Planning and Scheduling: Planning under Uncertainty
Planning and Scheduling: Real-time Planning