Multi-agent Communication with Graph Information Bottleneck under
Limited Bandwidth (a position paper)
- URL: http://arxiv.org/abs/2112.10374v1
- Date: Mon, 20 Dec 2021 07:53:44 GMT
- Title: Multi-agent Communication with Graph Information Bottleneck under
Limited Bandwidth (a position paper)
- Authors: Qi Tian, Kun Kuang, Baoxiang Wang, Furui Liu, Fei Wu
- Abstract summary: In many real-world scenarios, communication can be expensive and the bandwidth of the multi-agent system is subject to certain constraints.
Redundant messages who occupy the communication resources can block the transmission of informative messages and thus jeopardize the performance.
We propose a novel multi-agent communication module, CommGIB, which effectively compresses the structure information and node information in the communication graph to deal with bandwidth-constrained settings.
- Score: 92.11330289225981
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent studies have shown that introducing communication between agents can
significantly improve overall performance in cooperative Multi-agent
reinforcement learning (MARL). In many real-world scenarios, communication can
be expensive and the bandwidth of the multi-agent system is subject to certain
constraints. Redundant messages who occupy the communication resources can
block the transmission of informative messages and thus jeopardize the
performance. In this paper, we aim to learn the minimal sufficient
communication messages. First, we initiate the communication between agents by
a complete graph. Then we introduce the graph information bottleneck (GIB)
principle into this complete graph and derive the optimization over graph
structures. Based on the optimization, a novel multi-agent communication
module, called CommGIB, is proposed, which effectively compresses the structure
information and node information in the communication graph to deal with
bandwidth-constrained settings. Extensive experiments in Traffic Control and
StanCraft II are conducted. The results indicate that the proposed methods can
achieve better performance in bandwidth-restricted settings compared with
state-of-the-art algorithms, with especially large margins in large-scale
multi-agent tasks.
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