Accelerated Regularized Learning in Finite N-Person Games
Authors: Kyriakos Lotidis, Angeliki Giannou, Panayotis Mertikopoulos, Nicholas Bambos
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conclude this section with a series of numerical simulations to validate the performance of (FTXL). |
| Researcher Affiliation | Academia | Kyriakos Lotidis Stanford University klotidis@stanford.edu; Angeliki Giannou University of Wisconsin Madison giannou@wisc.edu; Panayotis Mertikopoulos Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP LIG 38000 Grenoble, France panayotis.mertikopoulos@imag.fr; Nicholas Bambos Stanford University bambos@stanford.edu |
| Pseudocode | Yes | π¦π,π+1 = π¦π,π+ πΎππ,π+1; ππ,π+1 = ππ,π+ πΎΛπ£π,π; π₯π,π= ππ(π¦π,π) . (FTXL) |
| Open Source Code | Yes | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [Yes] Justification: The code is included in the supplemental material. |
| Open Datasets | No | The paper does not use a publicly available or open dataset in the traditional sense for training. Instead, it defines the parameters of specific game types (zero-sum game, congestion game) for its numerical simulations. No concrete access information like a link, DOI, or citation to a pre-existing dataset is provided. |
| Dataset Splits | No | The paper defines specific game parameters for its simulations rather than using external datasets with explicit train/validation/test splits. It conducts '100 separate trials' but does not define a separate validation split within these trials. |
| Hardware Specification | Yes | The experiments have been implemented using Python 3.11.5 on a M1 Mac Book Air with 16GB of RAM. |
| Software Dependencies | Yes | The experiments have been implemented using Python 3.11.5 on a M1 Mac Book Air with 16GB of RAM. |
| Experiment Setup | Yes | For the zero-sum game, all runs were initialized with π¦1 = 0, and we used constant step-size πΎ= 10 2, and exploration parameter π= 10 1, where applicable. For the congestion game, the initial state π¦1 for each run was drawn uniformly at random in [ 1, 1]2, and we used constant step-size πΎ= 10 2, and exploration parameter ππ= 1/π1/4, where applicable. |