Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Multi-Agent Communication through Structured Attentive Reasoning
Authors: Murtaza Rangwala, Ryan Williams
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate the strength of our model in cooperative and competitive multi-agent tasks, where inter-agent communication and reasoning over prior information substantially improves performance compared to baselines. We conduct benchmarks on a mixture of discrete and continuous actions spaces, with limited or zero agent vision, using both REINFORCE [13] and our improved version of TD3 [12] to compare our approaches to relevant baselines. Our empirical study demonstrates the effectiveness of our novel architecture to solve cooperative and competitive multi-agent tasks with varying team sizes and environments. |
| Researcher Affiliation | Academia | Murtaza Rangwala Virginia Tech Blacksburg, VA 24060 EMAIL; Ryan Williams Virginia Tech Blacksburg, VA 24060 EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at: https://github.com/caslab-vt/SARNet |
| Open Datasets | Yes | We evaluate our communication architecture on Trafο¬c Junction and Open AI s multi-agent particle environment, [25], a two-dimensional stochastic environment consisting of agents and landmarks with cooperative tasks. |
| Dataset Splits | No | The paper describes training methodologies but does not provide specific dataset split information (percentages, sample counts, or explicit validation sets) needed to reproduce data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions the use of specific algorithms and environments (e.g., LSTM, REINFORCE, TD3, Open AI's multi-agent particle environment) but does not provide specific software library names with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | Hyperparameters are noted in Appendix A.2. Refer to Appendix A.1.3 for multi-agent trajectory-TD3. Environment details and additional experiments are described in Appendix B. |