VAIN: Attentional Multi-agent Predictive Modeling
Authors: Yedid Hoshen
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our method is evaluated on tasks from challenging multi-agent prediction domains: chess and soccer, and outperforms competing multi-agent approaches. |
| Researcher Affiliation | Industry | Yedid Hoshen Facebook AI Research, NYC yedidh@fb.com |
| Pseudocode | No | The paper provides a detailed schematic and descriptive steps for the VAIN architecture in Figure 1, but it is presented as a textual explanation rather than a formal, structured pseudocode block or algorithm labeled as such. For example, it says: 'Figure 1: A schematic of a single-hop VAIN: i) The agent features Fi are embedded by singleton encoder Es()...' |
| Open Source Code | No | The paper states 'All methods were implemented in Py Torch [34]', and reference [34] points to the PyTorch framework's GitHub repository. However, it does not explicitly state that the authors' own implementation code for VAIN is open-source or provide a link to their specific code repository. |
| Open Datasets | Yes | For training and evaluation of this task we downloaded 10k games from the FICS Games Dataset, an on-line repository of chess games. |
| Dataset Splits | No | The paper specifies training and evaluation sets (e.g., '9k randomly sampled games were used for training, and the remaining 1k games for evaluation'), and discusses test/train splits for soccer, but it does not explicitly mention a separate validation set used for hyperparameter tuning or model selection. |
| Hardware Specification | Yes | The chess prediction training for the MPP took several hours on a M40 GPU, other tasks had shorter training times due to smaller datasets. |
| Software Dependencies | No | The paper mentions 'All methods were implemented in Py Torch [34]' and refers to 'ADAM [35]' as the optimizer. While PyTorch is a software dependency, a specific version number for it is not provided, nor are version numbers for any other key software components or libraries. |
| Experiment Setup | Yes | The encoding and decoding functions Ec(), Es() and D() were implemented by fullyconnected neural networks with two layers, each of 256 hidden units and with Re LU activations. The encoder outputs had 128 units. |