  Effective communication protocols in multi-agent reinforcement learning
(MARL) are critical to fostering cooperation and enhancing team performance. To
leverage communication, many previous works have proposed to compress local
information into a single message and broadcast it to all reachable agents.
This simplistic messaging mechanism, however, may fail to provide adequate,
critical, and relevant information to individual agents, especially in severely
bandwidth-limited scenarios. This motivates us to develop context-aware
communication schemes for MARL, aiming to deliver personalized messages to
different agents. Our communication protocol, named CACOM, consists of two
stages. In the first stage, agents exchange coarse representations in a
broadcast fashion, providing context for the second stage. Following this,
agents utilize attention mechanisms in the second stage to selectively generate
messages personalized for the receivers. Furthermore, we employ the learned
step size quantization (LSQ) technique for message quantization to reduce the
communication overhead. To evaluate the effectiveness of CACOM, we integrate it
with both actor-critic and value-based MARL algorithms. Empirical results on
cooperative benchmark tasks demonstrate that CACOM provides evident performance
gains over baselines under communication-constrained scenarios. The code is
publicly available at https://github.com/LXXXXR/CACOM.
