Learning Individually Inferred Communication for Multi-Agent Cooperation
Authors: Ziluo Ding, Tiejun Huang, Zongqing Lu
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate I2C in three multi-agent cooperative tasks: cooperative navigation, predator prey, and traffic junction. For cooperative navigation and predator prey, I2C is built on MADDPG [10] to learn agent-agent communication. For traffic junction [19], our main purpose is to investigate the effectiveness of I2C on communication reduction, and thus we built I2C directly on Tar MAC [1] and it serves as communication control. In the experiments, I2C and baselines are parameter-sharing. |
| Researcher Affiliation | Academia | Ziluo Ding Tiejun Huang Zongqing Lu Peking University {ziluo,tjhuang,zongqing.lu}@pku.edu.cn |
| Pseudocode | No | The paper provides mathematical formulations for loss functions and gradients but does not include any structured pseudocode or algorithm blocks (e.g., a figure or section labeled 'Algorithm'). |
| Open Source Code | No | The paper does not provide any concrete access to source code, nor does it explicitly state that code will be released or is available. |
| Open Datasets | No | The paper uses standard multi-agent cooperative tasks (cooperative navigation, predator prey, traffic junction) which are simulation environments rather than discrete datasets. It does not provide specific links, DOIs, repository names, or formal citations with author/year for accessing the exact data used in their simulations. |
| Dataset Splits | No | The paper mentions 'three training runs' but does not provide specific dataset split information (exact percentages, sample counts, or citations to predefined splits) for training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific library versions) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions that hyperparameters are detailed in supplementary materials: 'Please refer to the supplementary for the hyperparameter settings.' Therefore, specific experimental setup details are not provided in the main text. |