  Communication can impressively improve cooperation in multi-agent
reinforcement learning (MARL), especially for partially-observed tasks.
However, existing works either broadcast the messages leading to information
redundancy, or learn targeted communication by modeling all the other agents as
targets, which is not scalable when the number of agents varies. In this work,
to tackle the scalability problem of MARL communication for partially-observed
tasks, we propose a novel framework Transformer-based Email Mechanism (TEM).
The agents adopt local communication to send messages only to the ones that can
be observed without modeling all the agents. Inspired by human cooperation with
email forwarding, we design message chains to forward information to cooperate
with the agents outside the observation range. We introduce Transformer to
encode and decode the message chain to choose the next receiver selectively.
Empirically, TEM outperforms the baselines on multiple cooperative MARL
benchmarks. When the number of agents varies, TEM maintains superior
performance without further training.
