Payoff-based Learning with Matrix Multiplicative Weights in Quantum Games
Authors: Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos, Jose Blanchet
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this last section, we provide numerical simulations to validate and explore the performance of (MMW) with payoff-based feedback. Additional experiments can be found in Appendix E. |
| Researcher Affiliation | Academia | Kyriakos Lotidis Stanford University klotidis@stanford.edu; Panayotis Mertikopoulos Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG 38000 Grenoble, France & Archimedes RU, NKUA panayotis.mertikopoulos@imag.fr; Nicholas Bambos Stanford University bambos@stanford.edu; Jose Blanchet Stanford University jose.blanchet@stanford.edu |
| Pseudocode | Yes | Algorithm 1: MMW with bandit feedback |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper describes a simulated game setup: 'Our testbed is a two-player zero-sum quantum game, which is the quantum analogue of a 2x2 min-max game with actions {a1, a2} and {b1, b2}, and payoff matrix P='. This is a custom-defined game environment, not a public dataset with explicit access information. |
| Dataset Splits | No | The paper describes an online learning process within a game theory context. It does not mention or utilize specific train/validation/test dataset splits in the conventional machine learning sense, as it's not evaluating models on static datasets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the numerical experiments (e.g., CPU, GPU, or memory specifications). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python version, library versions like PyTorch, TensorFlow, or specific solvers). |
| Experiment Setup | Yes | All the runs for the three different methods were initialized for Y = 0 and we used γ= 10−2 for all methods. In particular, for (3MW) with gradient estimates given by (2PE) estimator, we used a sampling radius δ= 10−2, and for (3MW) with (1PE) estimator, we used δ= 10−1 (in tune with our theoretical results which suggest the use of a tighter sampling radius when mixed payoff information is available to the players). |