Summary of Last-iterate Convergence Of General Parameterized Policies in Constrained Mdps, by Washim Uddin Mondal and Vaneet Aggarwal
Last-Iterate Convergence of General Parameterized Policies in Constrained MDPs
by Washim Uddin Mondal, Vaneet Aggarwal
First submitted to arxiv on: 21 Aug 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 proposed algorithm, Primal-Dual based Regularized Accelerated Natural Policy Gradient (PDR-ANPG), aims to learn Constrained Markov Decision Processes (CMDPs) via general parameterization. It uses entropy and quadratic regularizers to achieve this goal, ensuring a last-iterate optimality gap and constraint violation of ε up to an additive factor of εbias with sample complexity O(ε(-2)min{ε(-2),εbias^(1/3)}). For incomplete policy classes (εbias > 0), the sample complexity reduces to O(ε^(-2)) for ε < (εbias)^(1/6). The algorithm also achieves a last-iterate optimality gap and constraint violation of ε with sample complexity O(ε^(-4)) for complete policies with εbias = 0. This represents a significant improvement over the state-of-the-art guarantees for general parameterized CMDPs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to learn complex decision-making problems, called Constrained Markov Decision Processes (CMDPs). It uses a special algorithm called Primal-Dual based Regularized Accelerated Natural Policy Gradient (PDR-ANPG) to solve this problem. The goal is to find the best way to make decisions while following certain rules and constraints. The new algorithm is faster and more efficient than previous methods, allowing it to learn CMDPs with fewer examples. |
