Learning Efficient Multi-agent Communication: An Information Bottleneck Approach
Authors: Rundong Wang, Xu He, Runsheng Yu, Wei Qiu, Bo An, Zinovi Rabinovich
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |