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Being difficult to scale poses great problems in multi-agent coordination.Multi-agent Reinforcement Learning (MARL) algorithms applied in small-scalemulti-agent systems are hard to extend to large-scale ones because the latteris far more dynamic and the number of interactions increases exponentially withthe growing number of agents. Some swarm intelligence algorithms simulate therelease and utilization mechanism of pheromones to control large-scale agentcoordination. Inspired by such algorithms, \textbf{PooL}, an\textbf{p}her\textbf{o}m\textbf{o}ne-based indirect communication frameworkapplied to large scale multi-agent reinforcement \textbf{l}earning is proposedin order to solve the large-scale multi-agent coordination problem. Pheromonesreleased by agents of PooL are defined as outputs of most reinforcementlearning algorithms, which reflect agents' views of the current environment.The pheromone update mechanism can efficiently organize the information of allagents and simplify the complex interactions among agents into low-dimensionalrepresentations. Pheromones perceived by agents can be regarded as a summary ofthe views of nearby agents which can better reflect the real situation of theenvironment. Q-Learning is taken as our base model to implement PooL and PooLis evaluated in various large-scale cooperative environments. Experiments showagents can capture effective information through PooL and achieve higherrewards than other state-of-arts methods with lower communication costs.

PooL: Pheromone-inspired Communication Framework forLarge Scale Multi-Agent Reinforcement Learning