Impact of Decentralized Learning on Player Utilities in Stackelberg Games
Authors: Kate Donahue, Nicole Immorlica, Meena Jagadeesan, Brendan Lucier, Aleksandrs Slivkins
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We model these systems as Stackelberg games with decentralized learning and show that standard regret benchmarks (such as Stackelberg equilibrium payoffs) result in worst-case linear regret for at least one player. To better capture these systems, we construct a relaxed regret benchmark that is tolerant to small learning errors by agents. We show that standard learning algorithms fail to provide sublinear regret, and we develop algorithms to achieve nearoptimal O(T 2/3) regret for both players with respect to these benchmarks. |
| Researcher Affiliation | Collaboration | 1Cornell University 2Microsoft Research 3University of California, Berkeley. |
| Pseudocode | Yes | Algorithm 1: Explore Then Commit(E, C) (see e.g., (Slivkins, 2019; Lattimore and Szepesv ari, 2020))... Algorithm 2: Explore Then Commit Throw Out(E, E , C)... Algorithm 3: Explore Then UCB(E)... Algorithm 4: Lipschitz UCB(L, C)... Algorithm 5: Phased UCB(M1, . . . , MP )... Algorithm 6: Compute Active Arms(M1, . . . , MP , H)... Algorithm 7: Active Arm Elimination(M1, . . . , MP ) applied to (a, H) (adapted from (Even-Dar et al., 2002; Lattimore and Szepesv ari, 2020)) |
| Open Source Code | No | The paper mentions '1Full version: arxiv.org/abs/2403.00188.' which is a link to the full version of the paper on arXiv, a preprint server, not a code repository. There is no explicit statement about releasing source code for their methodology. |
| Open Datasets | No | The paper describes a theoretical model using abstract 'instances' (e.g., 'I = (A, B, v1, v2)'). It does not use or refer to any specific public datasets for training, nor does it provide access information for any datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets that would require training, validation, or test splits. No such information is provided. |
| Hardware Specification | No | The paper is purely theoretical and does not mention any hardware specifications used for running experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and proofs; it does not mention any specific software dependencies with version numbers for implementation or experimentation. |
| Experiment Setup | No | The paper is theoretical and discusses algorithmic parameters (e.g., E, E', M_i) as part of its algorithm definitions, not as experimental setup details like hyperparameters or training configurations for an empirical study. |