  Designing an effective communication mechanism among agents in reinforcement
learning has been a challenging task, especially for real-world applications.
The number of agents can grow or an environment sometimes needs to interact
with a changing number of agents in real-world scenarios. To this end, a
multi-agent framework needs to handle various scenarios of agents, in terms of
both scales and dynamics, for being practical to real-world applications. We
formulate the multi-agent environment with a different number of agents as a
multi-tasking problem and propose a meta reinforcement learning (meta-RL)
framework to tackle this problem. The proposed framework employs a meta-learned
Communication Pattern Recognition (CPR) module to identify communication
behavior and extract information that facilitates the training process.
Experimental results are poised to demonstrate that the proposed framework (a)
generalizes to an unseen larger number of agents and (b) allows the number of
agents to change between episodes. The ablation study is also provided to
reason the proposed CPR design and show such design is effective.
