Summary of Principal-agent Bandit Games with Self-interested and Exploratory Learning Agents, by Junyan Liu et al.
Principal-Agent Bandit Games with Self-Interested and Exploratory Learning Agents
by Junyan Liu, Lillian J. Ratliff
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper studies the repeated principal-agent bandit game, where an unknown environment is interacted with through incentives proposed by a self-interested learning agent. The agent learns the reward means and sometimes explores, which is motivated by online marketplaces. Algorithms are proposed for both i.i.d. and linear reward settings with bandit feedback in a finite horizon T, achieving regret bounds of O(sqrt(T)) and O(T^(2/3)), respectively. The algorithms rely on a novel elimination framework and newly-developed search algorithms that accommodate the uncertainty from the agent’s learning behavior. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers explore how to help an online marketplace agent learn about rewards while also exploring new possibilities. They develop special algorithms that work well in certain situations, like when rewards come from independent and identical distributions or are related to each other in a simple way. The goal is to minimize the regret, which means finding the best balance between learning and exploration. |
