Summary of Solving Multi-model Mdps by Coordinate Ascent and Dynamic Programming, By Xihong Su et al.
Solving Multi-Model MDPs by Coordinate Ascent and Dynamic Programming
by Xihong Su, Marek Petrik
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper introduces a novel framework for computing robust policies in Markov decision processes (MDPs) under parameter uncertainty. The Multi-model Markov Decision Process (MMDP) framework aims to find a policy that maximizes the expected return over a distribution of MDP models. To solve this NP-hard problem, most methods rely on approximations. This paper derives the policy gradient of MMDPs and proposes CADP, which combines coordinate ascent and dynamic programming to ensure monotone policy improvements to a local maximum. The authors also provide a theoretical analysis showing that CADP never performs worse than previous dynamic programming algorithms like WSU. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to find the best decisions in uncertain situations. It uses a special kind of math problem called Markov decision process (MDP) to help make smart choices when there’s uncertainty. The goal is to find the best choice that will give you the most reward. But this problem is really hard, so most people use shortcuts instead. This paper finds a new way to solve it by using two different methods: one helps us go up and another helps us look ahead. It shows that this new way works better than other ways on some tests. |
