  In cooperative multi-agent reinforcement learning (MARL), where agents only
have access to partial observations, efficiently leveraging local information
is critical. During long-time observations, agents can build \textit{awareness}
for teammates to alleviate the problem of partial observability. However,
previous MARL methods usually neglect this kind of utilization of local
information. To address this problem, we propose a novel framework, multi-agent
\textit{Local INformation Decomposition for Awareness of teammates} (LINDA),
with which agents learn to decompose local information and build awareness for
each teammate. We model the awareness as stochastic random variables and
perform representation learning to ensure the informativeness of awareness
representations by maximizing the mutual information between awareness and the
actual trajectory of the corresponding agent. LINDA is agnostic to specific
algorithms and can be flexibly integrated to different MARL methods. Sufficient
experiments show that the proposed framework learns informative awareness from
local partial observations for better collaboration and significantly improves
the learning performance, especially on challenging tasks.
