Opponent Modeling based on Subgoal Inference
Authors: XiaoPeng Yu, Jiechuan Jiang, Zongqing Lu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show that our method achieves more effective adaptation than existing methods in a variety of tasks. |
| Researcher Affiliation | Academia | Xiaopeng Yu Jiechuan Jiang Zongqing Lu School of Computer Science, Peking University |
| Pseudocode | Yes | For completeness, the full procedure of OMG is given in Algorithm 1. |
| Open Source Code | Yes | To ensure reproducibility, we include the code in the supplementary material and will make it open-source upon acceptance. |
| Open Datasets | Yes | Foraging [2, 4] is an 8 8 gridworld... Predator-Prey [20] is a three-against-one multi-agent environment... SMAC [35] is a high-dimensional environment for collaborative multi-agent reinforcement learning based on Star Craft II |
| Dataset Splits | No | The paper mentions 'training set' and 'test set' but does not explicitly specify a separate 'validation' set or its split percentages/counts for model selection during training. |
| Hardware Specification | Yes | The computational resources for the experiments are as follows: the CPU is Intel(R) Xeon(R) Platinum 8280 CPU @ 2.70GHz, and the GPU is A100-PCIE-40GB. |
| Software Dependencies | No | Table 1 lists various components like 'MLP', 'RNN', 'Re LU', 'Adam', 'RMSProp', 'CVAE' but does not provide specific version numbers for software libraries or dependencies (e.g., PyTorch 1.x, Python 3.x). |
| Experiment Setup | Yes | All hyperparameters are listed in Table 1. |