On the Convergence of No-Regret Learning Dynamics in Time-Varying Games
Authors: Ioannis Anagnostides, Ioannis Panageas, Gabriele Farina, Tuomas Sandholm
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, although the focus of this paper is theoretical, in this section we provide some illustrative experimental examples. In particular, Appendix B.1 contains experiments on time-varying potential games, while Appendix B.2 focuses on time-varying (two-player) zero-sum games. |
| Researcher Affiliation | Collaboration | Ioannis Anagnostides Carnegie Mellon University ianagnos@cs.cmu.edu Ioannis Panageas University of California Irvine ipanagea@ics.uci.edu Gabriele Farina MIT gfarina@mit.edu Tuomas Sandholm Carnegie Mellon University Strategic Machine, Inc. Strategy Robot, Inc. Optimized Markets, Inc. sandholm@cs.cmu.edu |
| Pseudocode | No | No |
| Open Source Code | No | No |
| Open Datasets | No | No |
| Dataset Splits | No | No |
| Hardware Specification | No | No |
| Software Dependencies | No | No |
| Experiment Setup | Yes | In our first experiment, we first sampled two matrices A, P Rdx dy, where dx = dy = 1000. Then, we defined each payoff matrix as A(t) := A(t 1) + Pt α for t 1, where A(0) := A. Here, α > 0 is a parameter that controls the variation of the payoff matrices. In this time-varying setup, we let each player employ (online) GD with learning rate η := 0.1. |