Paper ID: 2401.03504

ClusterComm: Discrete Communication in Decentralized MARL using Internal Representation Clustering

Robert Müller, Hasan Turalic, Thomy Phan, Michael Kölle, Jonas Nüßlein, Claudia Linnhoff-Popien

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.

Submitted: Jan 7, 2024