Computing Nash Equilibria in Potential Games with Private Uncoupled Constraints
Authors: Nikolas Patris, Stelios Stavroulakis, Fivos Kalogiannis, Rose Zhang, Ioannis Panageas
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically validate our theoretical results using constrained congestion games as a testing ground. |
| Researcher Affiliation | Academia | 1University of California, Irvine, 2Archimedes Research Unit |
| Pseudocode | Yes | Algorithm 1: IGDλ: Independent Gradient Descent on ϕ( ) = maxλ L( , λ) of the regularized Lagrangian L |
| Open Source Code | Yes | 1The code is available at the Git Hub repository: https://github.com/steliostavroulakis/constrained-potential-games |
| Open Datasets | No | The paper describes a simulation environment for 'constrained congestion games' with a 'rooted directed acyclic graph (DAG)' and 'five players' with defined 'congestion functions'. This describes the experimental setup and parameters, not a pre-existing or released dataset. |
| Dataset Splits | No | The paper describes metrics of convergence for evaluating the algorithm but does not mention specific training, validation, or test dataset splits, nor does it describe a cross-validation setup. |
| Hardware Specification | No | The paper describes its experimental setup in Section 5 but does not specify any particular hardware, such as GPU or CPU models, or cloud computing resources used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers, such as programming language versions, library versions, or specific solver versions. |
| Experiment Setup | Yes | Our experimental setup involves a rooted directed acyclic graph (DAG)... The graph comprises four paths connecting a source node s and a target node t... In addition, there are five players... The congestion experienced on each path is influenced by the number of players selecting that particular route... Let the learning rate η be 1/β, where β = c/µ and c = 4((n Amax)2 + (Λmaxγ)2) is constant. |