ALMA: Hierarchical Learning for Composite Multi-Agent Tasks
Authors: Shariq Iqbal, Robby Costales, Fei Sha
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate ALMA on two challenging environments, described below (more in Appendix B).In our experimental validation we aim to answer the following questions: 1) Learning Efficacy: Is ALMA effective in improving learning efficiency and asymptotic performance in comparison to state-of-the-art hierarchical and non-hierarchical MARL methods? |
| Researcher Affiliation | Collaboration | Shariq Iqbal Deepmind Robby Costales University of Southern California Fei Sha Google Research |
| Pseudocode | No | The paper describes its methods but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/shariqiqbal2810/ALMA |
| Open Datasets | Yes | STARCRAFT Our next environment is the Star Craft multi-agent challenge (SMAC) [22] |
| Dataset Splits | No | The paper discusses training and evaluation but does not specify explicit train/validation/test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, or memory specifications used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names like PyTorch or TensorFlow with their respective versions). |
| Experiment Setup | Yes | All exploration probabilities are annealed over the course of training, and the annealing schedules (along with all hyperparameters) are provided in Appendix D. |