Risk-Averse No-Regret Learning in Online Convex Games
Authors: Zifan Wang, Yi Shen, Michael Zavlanos
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate our method on an online market problem that we model as a Cournot game (Allaz & Vila, 1993). ... In section 5, we use an online market example to illustrate the effectiveness of the proposed algorithms. ... 5. Numerical Experiments |
| Researcher Affiliation | Academia | 1School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden. 2Department of Mechanical Engineering & Material Science, Duke University, Durham, NC 27708, USA. |
| Pseudocode | Yes | Algorithm 1 Risk-averse learning ... Algorithm 2 Risk-averse learning with sample reuse ... Algorithm 3 Risk-averse learning with residual feedback |
| Open Source Code | No | The paper does not provide any links or explicit statements about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes a synthetic Cournot game setup with a defined cost function and a uniform random variable for uncertainty, rather than using a publicly available or open dataset. For example: "The cost term ξixi models the uncertainty in the market, which is proportional to production." |
| Dataset Splits | No | The paper describes a numerical simulation setup for a Cournot game and does not specify training, validation, or test dataset splits, as it's not based on pre-existing data splits. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | In Algorithm 1, 2, and 3, the requirements are listed as: "Initial value x0, step size η, parameters a, b, δ, T, risk level αi, i = 1, , N.". In Section 5, "Numerical Experiments", it states: "We let α0 = 0.5 and α1 = 0.3, i.e, firm 1 is more risk sensitive than firm 0." and "We implement a hybrid sampling strategy for Algorithm 2 and select the switching time step as 15000". It also mentions: "All other parameters in Algorithms 1, 2 and 3 are tuned so that the three algorithms achieve individually their best performance." |