Learning to Communicate for Mobile Sensing with Multi-agent Reinforcement Learning

Mobile sensing has become a promising paradigm for monitoring the environmental state. When equipped with sensors, a group of unmanned vehicles can autonomously move around for distributed sensing. To maximize the sensing coverage, a critical challenge is to coordinate the decentralized vehicles for cooperation. In this work, we propose a novel algorithm Comm-Q, in which the vehicles can learn to communicate for cooperation via multi-agent reinforcement learning. At each step, the vehicles can broadcast a message to others, and condition on received aggregated message to update their sensing policies. The message is also learned via reinforcement learning. In addition, we decompose and reshape the reward function for more efficient policy training. Experimental results show that our algorithm is scalable and can converge very fast during training phase. It also outperforms other baselines significantly during execution. The results validate that communication message plays an important role to coordinate the behaviors of different vehicles.