No-Regret Learning in Unknown Games with Correlated Payoffs
Authors: Pier Giuseppe Sessa, Ilija Bogunovic, Maryam Kamgarpour, Andreas Krause
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally demonstrate the effectiveness of GP-MW in random matrix games, as well as real-world problems of traffic routing and movie recommendation. In our experiments, GP-MW consistently outperforms several baselines, while its performance is often comparable to methods that have access to full information feedback. |
| Researcher Affiliation | Academia | Pier Giuseppe Sessa ETH Zürich sessap@ethz.ch Ilija Bogunovic ETH Zürich ilijab@ethz.ch Maryam Kamgarpour ETH Zürich maryamk@ethz.ch Andreas Krause ETH Zürich krausea@ethz.ch |
| Pseudocode | Yes | Algorithm 1 The GP-MW algorithm for player i |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | We consider the Sioux-Falls road network [14, 1], a standard benchmark model in the transportation literature. Transportation network test problems. http://www.bgu.ac.il/ bargera/tntp/. We use the Movie Lens-100K dataset [12] which provides a matrix of ratings for 1682 movies rated by 943 users. |
| Dataset Splits | No | The paper describes the datasets used (random matrix games, Sioux-Falls road network, Movie Lens-100K) but does not provide explicit train/validation/test split percentages, sample counts, or cross-validation methodology. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers used for its implementation or experiments. |
| Experiment Setup | Yes | We select K = 30 and generate 10 random matrices with r1 = r2 GP(0, k( , )), where k = k SE with l = 6. We set the noise to i t N(0, 1), and use T = 200. We use the same set of algorithm parameters as in [8]. For every agent i to run GP-MW, we use a composite kernel ki such that for every a1, a2 2 A, ki((ai ), (a i 2 )) , where ki 1 is a linear kernel and ki 2 is a polynomial kernel of degree n 2 {2, 4, 6}. T = 100 game rounds. |