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Low-Bandwidth Communication Emerges Naturally in Multi-Agent Learning Systems
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In this work, we study emergent communication through the lens of cooperative multi-agent behavior in nature. Using insights from animal communication, we propose a spectrum from low-bandwidth (e.g. pheromone trails) to high-bandwidth (e.g. compositional language) communication that is based on the cognitive, perceptual, and behavioral capabilities of social agents. Through a series of experiments with pursuit-evasion games, we identify multi-agent reinforcement learning algorithms as a computational model for the low-bandwidth end of the communication spectrum.
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PILOC: A Pheromone Inverse Guidance Mechanism and Local-Communication Framework for Dynamic Target Search of Multi-Agent in Unknown Environments
A decentralized reinforcement-learning framework using virtual pheromone marks and local map sharing outperforms MARL baselines for finding dynamic targets in simulated unknown grid environments.
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