Multi-Agent Reinforcement Learning with Communication-Constrained Priors

Researchers propose a new multi-agent reinforcement learning framework that addresses communication constraints in real-world scenarios. The approach uses communication-constrained priors to distinguish between lossy and lossless messages, improving learning effectiveness in complex environments with unreliable communication.