Summary of Achieving Instance-dependent Sample Complexity For Constrained Markov Decision Process, by Jiashuo Jiang and Yinyu Ye
Achieving Instance-dependent Sample Complexity for Constrained Markov Decision Process
by Jiashuo Jiang, Yinyu Ye
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper tackles the reinforcement learning problem for Constrained Markov Decision Processes (CMDPs), a crucial issue in ensuring safety or resource constraints in sequential decision-making. In this scenario, agents are given finite resources and an unknown MDP with transition probabilities that need to be learned over time. The authors derive optimal guarantees for CMDP problems, achieving a logarithmic regret bound and sample complexity improvements upon previous state-of-the-art results. To achieve this, they develop a new framework for analyzing CMDPs, characterized by online primal LP resolution and adaptive remaining resource capacities. Key components include instance hardness characterization via LP basis, eliminating procedures identifying one optimal basis, and resolving procedures adapting to remaining resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how machines can learn to make decisions when there are limits on the amount of resources they have available. This is important for things like making sure a self-driving car doesn’t use up too much fuel or that a robot doesn’t waste too many batteries. The researchers come up with a new way to solve this problem, which involves learning over time and adapting to the limited resources. |
Keywords
* Artificial intelligence * Reinforcement learning
