  Learning communication via deep reinforcement learning (RL) or imitation
learning (IL) has recently been shown to be an effective way to solve
Multi-Agent Path Finding (MAPF). However, existing communication based MAPF
solvers focus on broadcast communication, where an agent broadcasts its message
to all other or predefined agents. It is not only impractical but also leads to
redundant information that could even impair the multi-agent cooperation. A
succinct communication scheme should learn which information is relevant and
influential to each agent's decision making process. To address this problem,
we consider a request-reply scenario and propose Decision Causal Communication
(DCC), a simple yet efficient model to enable agents to select neighbors to
conduct communication during both training and execution. Specifically, a
neighbor is determined as relevant and influential only when the presence of
this neighbor causes the decision adjustment on the central agent. This
judgment is learned only based on agent's local observation and thus suitable
for decentralized execution to handle large scale problems. Empirical
evaluation in obstacle-rich environment indicates the high success rate with
low communication overhead of our method.
