A priority-driven RL algorithm learns joint communication priorities and control policies for decentralized multi-agent systems in a model-free way and outperforms baselines on benchmark tasks.
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Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learning
A priority-driven RL algorithm learns joint communication priorities and control policies for decentralized multi-agent systems in a model-free way and outperforms baselines on benchmark tasks.