  Many multi-agent scenarios require message sharing among agents to promote
coordination, hastening the robustness of multi-agent communication when
policies are deployed in a message perturbation environment. Major relevant
works tackle this issue under specific assumptions, like a limited number of
message channels would sustain perturbations, limiting the efficiency in
complex scenarios. In this paper, we take a further step addressing this issue
by learning a robust multi-agent communication policy via multi-view message
certification, dubbed CroMAC. Agents trained under CroMAC can obtain guaranteed
lower bounds on state-action values to identify and choose the optimal action
under a worst-case deviation when the received messages are perturbed.
Concretely, we first model multi-agent communication as a multi-view problem,
where every message stands for a view of the state. Then we extract a
certificated joint message representation by a multi-view variational
autoencoder (MVAE) that uses a product-of-experts inference network. For the
optimization phase, we do perturbations in the latent space of the state for a
certificate guarantee. Then the learned joint message representation is used to
approximate the certificated state representation during training. Extensive
experiments in several cooperative multi-agent benchmarks validate the
effectiveness of the proposed CroMAC.
