Summary of Decentralized Online Learning in General-sum Stackelberg Games, by Yaolong Yu et al.
Decentralized Online Learning in General-Sum Stackelberg Games
by Yaolong Yu, Haipeng Chen
First submitted to arxiv on: 6 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 In this paper, researchers explore online learning strategies in general-sum Stackelberg games, where players make decisions without a central authority. They investigate two scenarios: one where the follower only receives its own reward and another where it has additional information about the leader’s reward. The team finds that for the first scenario, the best response to the leader’s action is the optimal strategy, but this isn’t always the case in the second scenario, where the follower can manipulate the leader’s rewards to achieve a better outcome. Building on these insights, the authors design algorithms for decentralized online learning in both settings and derive results for convergence and sample complexity. Notably, they propose a new manipulation strategy that outperforms the best response approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how players make decisions when there is no central authority guiding them. The researchers study two situations where one player (the follower) has limited information or extra information about what the other player (the leader) wants to achieve. They find that in some cases, the follower can use this information to influence the leader’s choices and get a better outcome. The authors then design ways for both players to make decisions online without a central authority, showing that these strategies work well in practice. |
Keywords
» Artificial intelligence » Online learning
