Contextual Games: Multi-Agent Learning with Side Information
Authors: Pier Giuseppe Sessa, Ilija Bogunovic, Andreas Krause, Maryam Kamgarpour
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We formulate the novel class of contextual games, a type of repeated games driven by contextual information at each round. By means of kernel-based regularity assumptions, we model the correlation between different contexts and game outcomes and propose a novel online (meta) algorithm that exploits such correlations to minimize the contextual regret of individual players. We define game-theoretic notions of contextual Coarse Correlated Equilibria (c-CCE) and optimal contextual welfare for this new class of games and show that c-CCEs and optimal welfare can be approached whenever players contextual regrets vanish. Finally, we empirically validate our results in a traffic routing experiment, where our algorithm leads to better performance and higher welfare compared to baselines that do not exploit the available contextual information or the correlations present in the game. |
| Researcher Affiliation | Academia | Pier Giuseppe Sessa ETH Zürich sessap@ethz.ch Ilija Bogunovic ETH Zürich ilijab@ethz.ch Andreas Krause ETH Zürich krausea@ethz.ch Maryam Kamgarpour ETH Zürich maryamk@ethz.ch |
| Pseudocode | Yes | Algorithm 1 The C.GP-MW (meta) algorithm |
| Open Source Code | No | The paper does not provide an explicit statement about making the source code available or a link to a code repository. |
| Open Datasets | Yes | We consider a contextual routing game on the traffic network of Sioux-Falls, a directed graph with 24 nodes and 76 edges, with the same game setup of [32] (network data and congestion model are taken from [25], while Appendix C gives a complete description of our experimental setup). |
| Dataset Splits | No | The paper describes a simulated repeated game environment but does not specify explicit training, validation, or test dataset splits in terms of percentages or sample counts. The evaluation is performed over the course of the game rounds. |
| Hardware Specification | No | The paper does not mention any specific hardware used for running the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper describes the algorithms and their theoretical guarantees but does not list specific software dependencies with version numbers, such as programming languages, libraries, or frameworks (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We set t according to Theorems 1 and 2, and βt = 2.0 (theoretical values for βt are found to be overly conservative [37, 32]). For rule (4) we set = 30|Ei|. Figure 1 shows the time-averaged losses (i.e., traveltimes scaled in [0, 1] and averaged over all the agents), which are inversely proportional to the game welfare, and the resulting network s congestion (computed as in Appendix C). |