Learning Multi-Agent Communication through Structured Attentive Reasoning
Authors: Murtaza Rangwala, Ryan Williams
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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 murtazar@vt.edu; Ryan Williams Virginia Tech Blacksburg, VA 24060 rywilli1@vt.edu |
| 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 Traffic 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. |