Summary of Sound Heuristic Search Value Iteration For Undiscounted Pomdps with Reachability Objectives, by Qi Heng Ho and Martin S. Feather and Federico Rossi and Zachary N. Sunberg and Morteza Lahijanian
Sound Heuristic Search Value Iteration for Undiscounted POMDPs with Reachability Objectives
by Qi Heng Ho, Martin S. Feather, Federico Rossi, Zachary N. Sunberg, Morteza Lahijanian
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Logic in Computer Science (cs.LO); Robotics (cs.RO); Systems and Control (eess.SY)
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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 investigates the Maximal Reachability Probability Problem (MRPP) in Partially Observable Markov Decision Processes (POMDPs), a challenging problem for sequential decision making under uncertainties. The goal is to maximize the probability of reaching target states without discounting. Building on point-based methods, the authors propose an algorithm that leverages value bounds and informed search for efficient exploration of belief spaces, providing two-sided bounds on optimal reachability probabilities. Experimental results show that this approach outperforms existing methods in most cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a math problem called Maximal Reachability Probability Problem (MRPP). It’s really hard to solve because it involves making decisions and guessing what might happen next, while also trying to get to certain goals. The researchers came up with a new way to solve this problem that works better than other methods they tried. They tested their idea on many different scenarios and found that it worked well in most cases. |
Keywords
» Artificial intelligence » Probability
