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Designing an effective communication mechanism among agents in reinforcementlearning has been a challenging task, especially for real-world applications.The number of agents can grow or an environment sometimes needs to interactwith a changing number of agents in real-world scenarios. To this end, amulti-agent framework needs to handle various scenarios of agents, in terms ofboth scales and dynamics, for being practical to real-world applications. Weformulate the multi-agent environment with a different number of agents as amulti-tasking problem and propose a meta reinforcement learning (meta-RL)framework to tackle this problem. The proposed framework employs a meta-learnedCommunication Pattern Recognition (CPR) module to identify communicationbehavior 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 ofagents to change between episodes. The ablation study is also provided toreason the proposed CPR design and show such design is effective.

Meta-CPR: Generalize to Unseen Large Number of Agents with Communication Pattern Recognition Module