DAG-Based Column Generation for Adversarial Team Games
Authors: Youzhi Zhang, Bo An, Daniel Dajun Zeng
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Experimental Evaluation We evaluate the performance of DCG and run all experiments on a machine with a 4-core 2.3GHz CPU (8 threads) and 16GB of RAM available by using CPLEX 20.1. |
| Researcher Affiliation | Collaboration | 1Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences 2Nanyang Technological University, Singapore 3Skywork AI, Singapore 4Institute of Automation, Chinese Academy of Sciences, Beijing, China. |
| Pseudocode | Yes | Algorithm 1 Expanding the TB-DAG |
| Open Source Code | No | No explicit statement or link providing access to the authors' source code was found. The paper mentions that 'the codes for these baselines are not available'. |
| Open Datasets | Yes | We then use two standard extensive-form games (Farina et al., 2018; 2021; Carminati et al., 2022): Kuhn poker and Leduc poker (details on them can be found in these references). |
| Dataset Splits | No | The paper discusses extensive-form games (Kuhn poker and Leduc poker) which are game environments, not typical datasets that are split into train/validation/test sets for machine learning models. No explicit mention of data splits was found. |
| Hardware Specification | Yes | We evaluate the performance of DCG and run all experiments on a machine with a 4-core 2.3GHz CPU (8 threads) and 16GB of RAM available by using CPLEX 20.1. |
| Software Dependencies | Yes | We evaluate the performance of DCG and run all experiments on a machine with a 4-core 2.3GHz CPU (8 threads) and 16GB of RAM available by using CPLEX 20.1. |
| Experiment Setup | Yes | For these CG-based algorithms, we randomly initialize the restricted game with a coordinated strategy for the team, and for the CG-based algorithms with semi-randomized strategies, we initialize a uniform strategy for the corresponding player. For each of these algorithms, we consider one more variant: at each iteration, it solves the linear relaxation of the BRO first to see if it can output the optimal solution added to the restricted game; if it cannot do that, it solves the original mixed-integer BRO and adds all feasible solutions for the BRO from the CPLEX solution pool to the original game. ...with target precision of the team value in a TMECor is 10^-6 |