Scaling Up Multiagent Reinforcement Learning for Robotic Systems: Learn an Adaptive Sparse Communication Graph
Name
2003.01040.pdf
Description
Submitted version
Size
469.67 KB
Format
Adobe PDF
Checksum (MD5)
f0bfe1ba914d44f3c4a3563f6ade49ad
Author(s)
Sun, Chuangchuang
Shen, Macheng
How, Jonathan P.
Date Issued
October 24, 2020
Journal
IEEE International Conference on Intelligent Robots and Systems
Publisher
IEEE
Citation
Sun, Chuangchuang, Shen, Macheng and How, Jonathan P. 2020. "Scaling Up Multiagent Reinforcement Learning for Robotic Systems: Learn an Adaptive Sparse Communication Graph." IEEE International Conference on Intelligent Robots and Systems.
Version
Original manuscript
Abstract
© 2020 IEEE. The complexity of multiagent reinforcement learning (MARL) in multiagent systems increases exponentially with respect to the agent number. This scalability issue prevents MARL from being applied in large-scale multiagent systems. However, one critical feature in MARL that is often neglected is that the interactions between agents are quite sparse. Without exploiting this sparsity structure, existing works aggregate information from all of the agents and thus have a high sample complexity. To address this issue, we propose an adaptive sparse attention mechanism by generalizing a sparsity-inducing activation function. Then a sparse communication graph in MARL is learned by graph neural networks based on this new attention mechanism. Through this sparsity structure, the agents can communicate in an effective as well as efficient way via only selectively attending to agents that matter the most and thus the scale of the MARL problem is reduced with little optimality compromised. Comparative results show that our algorithm can learn an interpretable sparse structure and outperforms previous works by a significant margin on applications involving a large-scale multiagent system.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iros45743.2020.9341303
