Game Redesign in No-regret Game Playing
Authors: Yuzhe Ma, Young Wu, Xiaojin Zhu
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulations on four classic games confirm the effectiveness of our proposed redesign algorithms. |
| Researcher Affiliation | Academia | Yuzhe Ma , Young Wu , Xiaojin Zhu University of Wisconsin Madison {yzm234, yw, jerryzhu}@cs.wisc.edu |
| Pseudocode | Yes | Algorithm 1 Interior Design; Algorithm 2 Boundary Design; Algorithm 3 Discrete Design |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper defines and simulates classic game theory scenarios (e.g., Volunteer's Dilemma, RPS) within the paper itself or by reference to well-known theoretical constructs. It does not use or link to external, publicly available datasets in the traditional sense that require concrete access information. |
| Dataset Splits | No | The paper describes running simulations for a number of rounds (T) and analyzing the outcomes, rather than using a train/validation/test split of a dataset. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU/CPU models or cloud resources. |
| Software Dependencies | No | The paper mentions using 'EXP3.P [Bubeck and Cesa-Bianchi, 2012]' as the no-regret learner but does not specify a version number for it or for any other key software components or libraries used. |
| Experiment Setup | Yes | The margin parameter is ρ = 1. We ran Algorithm 2 for ϵ = 0.1, 0.2, 0.3, 0.4. For each ϵ we simulated game play for T = 104, 105, 106 and 107. |