Summary of Combinatorial Multivariant Multi-armed Bandits with Applications to Episodic Reinforcement Learning and Beyond, by Xutong Liu et al.
Combinatorial Multivariant Multi-Armed Bandits with Applications to Episodic Reinforcement Learning and Beyond
by Xutong Liu, Siwei Wang, Jinhang Zuo, Han Zhong, Xuchuang Wang, Zhiyong Wang, Shuai Li, Mohammad Hajiesmaili, John C.S. Lui, Wei Chen
First submitted to arxiv on: 3 Jun 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 This novel framework combines combinatorial multi-armed bandits (CMAB) with multivariant and probabilistically triggering arms (CMAB-MT), enhancing modeling power while leveraging distinct statistical properties. The CMAB-MT framework is built upon a general 1-norm multivariant and triggering probability-modulated smoothness condition, allowing for improved results in applications like episodic reinforcement learning (RL) and probabilistic maximum coverage for goods distribution. By bridging the gap between CMAB and RL, this work encourages interactions between these two important directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to solve problems by combining two important areas: combinatorial multi-armed bandits (CMAB) and episodic reinforcement learning (RL). It uses a special type of math called multivariant random variables to make better decisions. This framework can be used for things like deciding which goods to deliver or choosing the best actions in games. |
Keywords
» Artificial intelligence » Probability » Reinforcement learning
