Summary of Adaptive Discretization Using Voronoi Trees For Continuous Pomdps, by Marcus Hoerger et al.
Adaptive Discretization using Voronoi Trees for Continuous POMDPs
by Marcus Hoerger, Hanna Kurniawati, Dirk Kroese, Nan Ye
First submitted to arxiv on: 21 Feb 2023
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel online solver for Partially Observable Markov Decision Processes (POMDPs) is proposed, addressing the challenge of solving POMDPs with high-dimensional continuous action spaces. The Adaptive Discretization using Voronoi Trees (ADVT) method combines Monte Carlo Tree Search and an adaptive discretization approach to efficiently sample these action spaces and compute optimal actions. ADVT adaptsively discretizes the action space for each sampled belief, using a hierarchical partition called Voronoi trees, which maintains the partition of cells as the Voronoi diagram of two points sampled from the cell. This allows ADVT to exploit local information and identify promising regions in the action space faster than existing solvers. The method also handles continuous observation spaces, adopting an observation progressive widening strategy and a weighted particle representation of beliefs. Experimental results show that ADVT scales better to high-dimensional continuous action spaces compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to solve POMDPs is discovered! This technique helps computers make smart decisions when there’s lots of information to consider. It uses special maps called Voronoi trees to break down big problems into smaller ones, making it easier to find the best solution. This method also works with observations that are not exact, which makes it very useful in many real-world situations. The results show that this technique is much faster and more efficient than other methods, especially when dealing with really complex decisions. |
