  Exploration in decentralized cooperative multi-agent reinforcement learning
faces two challenges. One is that the novelty of global states is unavailable,
while the novelty of local observations is biased. The other is how agents can
explore in a coordinated way. To address these challenges, we propose MACE, a
simple yet effective multi-agent coordinated exploration method. By
communicating only local novelty, agents can take into account other agents'
local novelty to approximate the global novelty. Further, we newly introduce
weighted mutual information to measure the influence of one agent's action on
other agents' accumulated novelty. We convert it as an intrinsic reward in
hindsight to encourage agents to exert more influence on other agents'
exploration and boost coordinated exploration. Empirically, we show that MACE
achieves superior performance in three multi-agent environments with sparse
rewards.
