Summary of Efficient Constraint Generation For Stochastic Shortest Path Problems, by Johannes Schmalz et al.
Efficient Constraint Generation for Stochastic Shortest Path Problems
by Johannes Schmalz, Felipe Trevizan
First submitted to arxiv on: 26 Jan 2024
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 This paper addresses the issue of unnecessary computation in solving Stochastic Shortest Path Problems (SSPs) by introducing an efficient version of constraint generation. The current methods for finding states’ costs-to-go involve applying Bellman backups, but these algorithms need to compute the cost-to-go for every applicable action during each state backup, leading to unnecessary computation for actions identified as sub-optimal. The authors present new connections between planning and operations research and develop a novel technique that allows algorithms to ignore sub-optimal actions and avoid computing their costs-to-go. This technique is applied to iLAO, resulting in a new algorithm called CG-iLAO. Experimental results show that CG-iLAO* can ignore up to 57% of iLAO’s actions, solving problems up to 8x and 3x faster than LRTDP and iLAO. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces an efficient way to solve Stochastic Shortest Path Problems. Right now, computers take a long time to find the best path because they have to check many options that won’t work out. The authors found a better way to do this by ignoring paths that won’t help and only checking the ones that might actually be good. This makes the computer go much faster and can even solve problems that were too hard for it before. |
