Summary of Contextual Restless Multi-armed Bandits with Application to Demand Response Decision-making, by Xin Chen et al.
Contextual Restless Multi-Armed Bandits with Application to Demand Response Decision-Making
by Xin Chen, I-Hong Hou
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This paper presents a new approach to online decision-making called Contextual Restless Bandits (CRB). It combines two existing methods, contextual bandits and restless bandits, allowing it to account for both internal state changes in each arm and external environmental context influences. The authors develop an index policy algorithm using dual decomposition, which they analyze theoretically for asymptotic optimality. When the arm models are unknown, a model-based online learning algorithm is proposed to learn the models and make decisions simultaneously. The CRB framework is applied to demand response decision-making in smart grids, with numerical simulations demonstrating its performance and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to make decisions online called Contextual Restless Bandits (CRB). It’s like a combination of two other ways people decide things: one that looks at what’s happening inside each option, and another that considers outside factors. The researchers created an algorithm that works well with this new approach and showed it’s efficient in real-world applications. |
Keywords
» Artificial intelligence » Online learning
