  In Multi-Agent Reinforcement Learning, communication is critical to encourage
cooperation among agents. Communication in realistic wireless networks can be
highly unreliable due to network conditions varying with agents' mobility, and
stochasticity in the transmission process. We propose a framework to learn
practical communication strategies by addressing three fundamental questions:
(1) When: Agents learn the timing of communication based on not only message
importance but also wireless channel conditions. (2) What: Agents augment
message contents with wireless network measurements to better select the game
and communication actions. (3) How: Agents use a novel neural message encoder
to preserve all information from received messages, regardless of the number
and order of messages. Simulating standard benchmarks under realistic wireless
network settings, we show significant improvements in game performance,
convergence speed and communication efficiency compared with state-of-the-art.

Artificial Intelligence;Machine Learning;Multiagent Systems