Decentralised Learning in Systems With Many, Many Strategic Agents
Authors: David Mguni, Joel Jennings, Enrique Munoz de Cote
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate these theoretical results by showing convergence to Nash-equilibrium policies in applications from economics and control theory with thousands of strategically interacting agents. Experiment 1: Spatial Congestion Game. Experiment 2: Supply with Uncertain Demand. Experiment 3: Mean-Field Linear Quadratic Regulator. |
| Researcher Affiliation | Collaboration | 1PROWLER.io, Cambridge, UK 2Department of Computer Science, INAOE, Mexico |
| Pseudocode | No | The paper describes the learning procedure but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes experiment setups based on benchmark *problem types* but does not specify using or providing access to any publicly available *datasets* in the traditional sense. For example, for the Spatial Congestion Game, it defines reward functions and transition dynamics with parameters, rather than referencing a specific dataset file. |
| Dataset Splits | No | The paper does not specify explicit dataset splits (e.g., percentages or counts) for training, validation, or testing data. It mentions |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Experiment 1: Spatial Congestion Game: α {1.0, 2.0, 3.0}, μ R2, Σ 12 2, R = η1(2 2), σ1, A1, B1 1(2 2) and c, σϵ R+. Experiment 2: Supply with Uncertain Demand: fixed number (30) time steps. Experiment 3: Mean-Field Linear Quadratic Regulator: C(xt, mxt) (xt α)T Qt(xt α), ut R2 1, A1, B1 1(2 2), σ1 = c1(2 2). |