Summary of Pessimistic Iterative Planning For Robust Pomdps, by Maris F. L. Galesloot et al.
Pessimistic Iterative Planning for Robust POMDPs
by Maris F. L. Galesloot, Marnix Suilen, Thiago D. Simão, Steven Carr, Matthijs T. J. Spaan, Ufuk Topcu, Nils Jansen
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 The abstract presents a novel approach to Robust Partially Observable Markov Decision Processes (POMDPs), which extends classical POMDPs to handle model uncertainty. The authors propose the Pessimistic Iterative Planning (PIP) framework, which alternates between selecting a pessimistic POMDP and computing a finite-state controller for it. They also introduce the rFSCNet algorithm, a recurrent neural network that finds an FSC through supervision policies optimized for the pessimistic POMDP. The authors evaluate their approach in four benchmark environments, demonstrating improved robustness against several baseline methods and competitive performance compared to a state-of-the-art robust POMDP solver. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making decision-making systems more reliable by accounting for uncertainty in how things work and happen. Imagine you’re trying to get from point A to point B, but you’re not sure which path to take because the map might be incomplete or wrong. That’s kind of like what this paper is doing – it’s developing a new way to make decisions when there’s uncertainty involved. The authors are using something called POMDPs (Partial Observable Markov Decision Processes) and making them more robust by considering different possible scenarios. They’re also testing their approach in different situations to see how well it works. |
Keywords
» Artificial intelligence » Neural network
