Summary of Approximate Control For Continuous-time Pomdps, by Yannick Eich et al.
Approximate Control for Continuous-Time POMDPs
by Yannick Eich, Bastian Alt, Heinz Koeppl
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); Quantitative Methods (q-bio.QM)
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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 decision-making framework is proposed for partially observable systems with discrete state and action spaces in continuous time. The framework employs approximation methods that scale well with increasing state space sizes, making it feasible for large-scale applications. Specifically, the filtering distribution is approximated by projecting it onto a parametric family of distributions, which is then integrated into a control heuristic based on the fully observable system to obtain a scalable policy. The effectiveness of the approach is demonstrated through simulations of various partially observed systems, including queueing systems and chemical reaction networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make decisions in complex systems that can’t be fully seen or controlled. It’s like trying to find your way out of a maze without being able to see the whole thing. The researchers developed a shortcut to make better choices by approximating some really complicated math problems. They tested this approach on different types of systems, such as traffic flow and chemical reactions, and showed that it works well even when things get really complex. |
