Online Learning in Stackelberg Games with an Omniscient Follower
Authors: Geng Zhao, Banghua Zhu, Jiantao Jiao, Michael Jordan
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the problem of online learning in a two-player decentralized cooperative Stackelberg game. We analyze the sample complexity of regret minimization in this repeated Stackelberg game. We show that depending on the reward structure, the existence of the omniscient follower may change the sample complexity drastically, from constant to exponential, even for linear cooperative Stackelberg games. This poses unique challenges for the learning process of the leader and the subsequent regret analysis. |
| Researcher Affiliation | Academia | 1Department of EECS, University of California, Berkeley, USA. |
| Pseudocode | Yes | Algorithm 1 UCB with side information from expert |
| Open Source Code | No | The paper does not provide any specific links or statements about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper focuses on theoretical analysis and does not mention the use of specific datasets for training. |
| Dataset Splits | No | The paper focuses on theoretical analysis and does not discuss training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical analysis and does not include specific experimental setup details, hyperparameters, or training configurations. |