Summary of Truncated Variance Reduced Value Iteration, by Yujia Jin et al.
Truncated Variance Reduced Value Iteration
by Yujia Jin, Ishani Karmarkar, Aaron Sidford, Jiayi Wang
First submitted to arxiv on: 21 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); Optimization and Control (math.OC)
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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 presents faster randomized algorithms for computing an epsilon-optimal policy in discounted Markov decision processes with large state-action spaces. The proposed algorithms improve upon existing methods by reducing computational time from at least quadratic to nearly linear, making them more scalable for real-world applications. The authors achieve this breakthrough by building upon prior stochastic variance-reduced value iteration methods and introducing new variance-reduced sampling procedures. These advancements have the potential to significantly narrow the gap between model-free and model-based methods in solving complex decision-making problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us find the best way to make decisions when we don’t know everything about a situation. It uses special math called Markov decision processes to solve this problem. The new algorithms are much faster than before, which means they can be used for bigger and more complicated situations. This is important because it can help us make better choices in areas like business or healthcare. The researchers did this by building on previous ideas and coming up with new ways to reduce the uncertainty when making decisions. |
