Exploiting Opponents Under Utility Constraints in Sequential Games
Authors: Martino Bernasconi-de-Luca, Federico Cacciamani, Simone Fioravanti, Nicola Gatti, Alberto Marchesi, Francesco Trovò
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically evaluate the convergence of our algorithm on standard testbeds of sequential games. |
| Researcher Affiliation | Academia | Martino Bernasconi-de-Luca Politecnico di Milano Federico Cacciamani Politecnico di Milano Simone Fioravanti Gran Sasso Science Institute Nicola Gatti Politecnico di Milano Alberto Marchesi Politecnico di Milano Francesco Trovò Politecnico di Milano |
| Pseudocode | Yes | Algorithm 1 COX-UCB Algorithm 2 Strategy selection of COX-UCB Algorithm 3 Strategy selection of ψ-COX-UCB |
| Open Source Code | No | The paper does not contain any explicit statement about making the source code available, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper mentions using "standard testbed of Kuhn and Leduc poker games [Kuhn, 2016]" but does not provide a direct link, DOI, or a formal citation with author names and year in brackets/parentheses for accessing the dataset itself. The citation points to a book chapter, not a dataset source. |
| Dataset Splits | No | The paper mentions evaluating against "10 different randomly generated strategies" and executing "5 different algorithm runs," but it does not specify any training, validation, or test dataset splits in terms of percentages or sample counts from a predefined dataset. The data is generated through online game play rather than being split from a static dataset. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as GPU/CPU models, memory, or cloud instance types. |
| Software Dependencies | Yes | We use Gurobi for solving bilinear optimization problems [Gurobi Optimization, 2021]. |
| Experiment Setup | Yes | The values (α, β) needed for the utility constraints are set to α = 0.3 and β = 0.3 in all the experiments. |