Summary of Learning Constrained Markov Decision Processes with Non-stationary Rewards and Constraints, by Francesco Emanuele Stradi et al.
Learning Constrained Markov Decision Processes With Non-stationary Rewards and Constraints
by Francesco Emanuele Stradi, Anna Lunghi, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The paper presents algorithms for constrained Markov decision processes (CMDPs) with non-stationary rewards and constraints. It shows that by providing algorithms whose performances smoothly degrade as non-stationarity increases, the negative result of attaining both sublinear regret and sublinear constraint violation can be eased. Specifically, the proposed algorithms attain ( + C) regret and positive constraint violation under bandit feedback, where C is a corruption value measuring environment non-stationarity. The results are of independent interest and can be applied to any non-stationary constrained online learning setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about machines that make decisions while following certain rules (constrained Markov decision processes). It shows that if the rewards and rules change over time, it’s hard for these machines to do well. But the researchers found a way to create algorithms that can still do okay even when this happens. They came up with two ways to solve this problem: one where you know how much the environment is changing, and another where you don’t know. This could be useful in lots of different situations. |
Keywords
» Artificial intelligence » Online learning
