Scaling Up Multiagent Reinforcement Learning for Robotic Systems: Learn an Adaptive Sparse Communication Graph

• URL: http://arxiv.org/abs/2003.01040v2
• Date: Tue, 3 Mar 2020 21:53:06 GMT
• Title: Scaling Up Multiagent Reinforcement Learning for Robotic Systems: Learn an Adaptive Sparse Communication Graph
• Authors: Chuangchuang Sun, Macheng Shen, and Jonathan P. How
• Abstract summary: The complexity of multiagent reinforcement learning increases exponentially with respect to the agent number. One critical feature in MARL that is often neglected is that the interactions between agents are quite sparse. We propose an adaptive sparse attention mechanism by generalizing a sparsity-inducing activation function. We 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.
• Score: 39.48317026356428
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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