Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach
Authors: Rundong Wang, Xu He, Runsheng Yu, Wei Qiu, Bo An, Zinovi Rabinovich
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 19,006 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,883 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To demonstrate the efficiency of our method, we conduct extensive experiments in various cooperative and competitive multi-agent tasks with different numbers of agents and different bandwidths. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Nanyang Technological University, Singapore. |
| Pseudocode | Yes | Algorithm 1: Informative Multi-agent Communication |
| Open Source Code | Yes | 1Our code is provided in https://github.com/EC2EZ4RD/IMAC |
| Open Datasets | Yes | We evaluate IMAC on a variety of tasks and environments: cooperative navigation and predator prey in Multi Particle Environment (Lowe et al., 2017), as well as 3m and 8m in Star Craft II (Samvelyan et al., 2019). |
| Dataset Splits | No | The paper evaluates agents in simulated environments where data is generated during interaction, rather than using predefined dataset splits. No explicit train/validation/test splits are mentioned for a fixed dataset. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | We first respectively train IMAC with different prior distributions z(Mi) of N(0, 1), N(0, 5), and N(0, 10), to satisfy different default limited bandwidth constraints. We train our agents by self-play for 100,000 episodes. Ablation studies also provide details on parameters like β. |