Evolution Strategies for Approximate Solution of Bayesian Games
Authors: Zun Li, Michael P. Wellman5531-5540
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our methods on MBS and HG environments of 5, 10, and 15 agents with the same number of goods. Each experiment runs for 5 trials. The results are shown in Figures 2 and 3. |
| Researcher Affiliation | Academia | Zun Li and Michael P. Wellman University of Michigan, Ann Arbor {lizun, wellman}@umich.edu |
| Pseudocode | Yes | Algorithm 1: Natural Evolution Strategies; Algorithm 2: Minimax-NES for PBNE; Algorithm 3: Incremental Strategy Generation |
| Open Source Code | Yes | Li, Z.; and Wellman, M. P. 2021. Evolution strategies for approximate solution of Bayesian games: Supplementary material. Avaliable at https://rezunli96.github.io/. |
| Open Datasets | No | The paper describes the setup of simulated environments (MBS and HG) where types are drawn from distributions, but it does not refer to a pre-existing publicly available dataset with concrete access information (link, DOI, or formal citation). |
| Dataset Splits | No | The paper does not explicitly provide training, validation, and test splits with specific percentages, absolute sample counts, or references to predefined splits for the main experimental evaluation. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments, such as specific GPU/CPU models, processors, or memory amounts. It only discusses the computational methods. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and 'two-layer perceptron' but does not specify their version numbers or the versions of any underlying programming languages or libraries used for implementation. |
| Experiment Setup | Yes | The hyperparameters for NES are tuned as follows. We fixed population size J = 4 + 3 log d as the default setting adopted by Wierstra et al. (2014), where d is the number of parameters of the deep model. For every fitness function we apply a grid search to select the best bandwidth ν and learning rate α within certain ranges, to maximize the performance of the resulted NES. ... For black-box functions O( , s) and O( , σ), each query we run an agent-based simulation for 5000 times and take the corresponding average payoff values as the outputs. |