Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning

Understand

Communication is a important factor that enables agents work cooperatively in multi-agent reinforcement learning (MARL).

• Most previous work uses continuous message communication whose high representational capacity comes at the expense of interpretability.
• Allowing agents to learn their own discrete message communication protocol emerged from a variety of domains can increase the interpretability for human designers and other agents.This paper proposes a method to generate discrete messages analogous to human languages, and achieve communication by a broadcast-and-listen mechanism based on self-attention.
• We show that discrete message communication has performance comparable to continuous message communication but with much a much smaller vocabulary size.Furthermore, we propose an approach that allows humans to interactively send discrete messages to agents.