Model-Based Opponent Modeling
Authors: XiaoPeng Yu, Jiechuan Jiang, Wanpeng Zhang, Haobin Jiang, Zongqing Lu
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show that MBOM achieves more effective adaptation than existing methods in a variety of tasks, respectively with different types of opponents, i.e., fixed policy, naïve learner, and reasoning learner. |
| Researcher Affiliation | Academia | Xiaopeng Yu Jiechuan Jiang Wanpeng Zhang Haobin Jiang Zongqing Lu School of Computer Science, Peking University |
| Pseudocode | Yes | Algorithm 1 MBOM |
| Open Source Code | No | The code for the main experiments will be released after acceptance. |
| Open Datasets | Yes | Triangle Game is an asymmetric zero-sum game implemented on Multi-Agent Particle Environments (MPE) [21]. |
| Dataset Splits | No | The paper describes interacting with environments like MPE and Google Research Football but does not provide explicit training, validation, and test dataset splits with percentages or sample counts, as these are simulated environments rather than static datasets. |
| Hardware Specification | Yes | All the experiments were run on NVIDIA GeForce RTX 3090 GPUs. |
| Software Dependencies | Yes | We use PyTorch (v1.8.0) to implement our method and baselines. |
| Experiment Setup | Yes | More details about experimental settings and hyperparameters are available in Appendix E. |