Summary of Cooperative Multi-agent Constrained Stochastic Linear Bandits, by Amirhossein Afsharrad et al.
Cooperative Multi-Agent Constrained Stochastic Linear Bandits
by Amirhossein Afsharrad, Parisa Oftadeh, Ahmadreza Moradipari, Sanjay Lall
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multiagent Systems (cs.MA); Machine Learning (stat.ML)
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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 explores a collaborative multi-agent setting where N agents communicate locally to minimize their collective regret while keeping their expected cost under a specified threshold . Each agent faces a distinct linear bandit problem, and the goal is to determine the best overall action. The authors propose the MA-OPLB algorithm, which utilizes an accelerated consensus method to estimate average rewards and costs across the network. They establish a high probability bound on the T-round regret of MA-OPLB, showing it scales with (). The algorithm is tested on various network structures, demonstrating its effectiveness. This paper contributes to the development of distributed algorithms for multi-agent systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a group of agents working together to make decisions. Each agent has its own problem to solve, but they need to cooperate to achieve their shared goal. The authors created an algorithm called MA-OPLB that helps these agents work together effectively. They tested this algorithm in different situations and showed that it can help the agents make good decisions quickly. This research is important for developing better ways for groups of agents to work together. |
Keywords
» Artificial intelligence » Probability
