ClusterComm: Discrete Communication in Decentralized MARL using Internal Representation Clustering

• URL: http://arxiv.org/abs/2401.03504v1
• Date: Sun, 7 Jan 2024 14:53:43 GMT
• Title: ClusterComm: Discrete Communication in Decentralized MARL using Internal Representation Clustering
• Authors: Robert M\"uller, Hasan Turalic, Thomy Phan, Michael K\"olle, Jonas N\"u{\ss}lein, Claudia Linnhoff-Popien
• Abstract summary: ClusterComm is a fully decentralized MARL framework where agents communicate discretely without a central control unit. Mini-Batch-K-Means clustering on the last hidden layer's activations of an agent's policy network translates them into discrete messages.
• Score: 6.839032445412096
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the realm of Multi-Agent Reinforcement Learning (MARL), prevailing approaches exhibit shortcomings in aligning with human learning, robustness, and scalability. Addressing this, we introduce ClusterComm, a fully decentralized MARL framework where agents communicate discretely without a central control unit. ClusterComm utilizes Mini-Batch-K-Means clustering on the last hidden layer's activations of an agent's policy network, translating them into discrete messages. This approach outperforms no communication and competes favorably with unbounded, continuous communication and hence poses a simple yet effective strategy for enhancing collaborative task-solving in MARL.

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