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Multi-agent foraging (MAF) involves distributing a team of agents to searchan environment and extract resources from it. Nature provides several examplesof highly effective foragers, where individuals within the foraging collectiveuse biological markers (e.g., pheromones) to communicate critical informationto others via the environment. In this work, we propose ForMIC, a distributedreinforcement learning MAF approach that endows agents with implicitcommunication abilities via their shared environment. However, learningefficient policies with stigmergic interactions is highly nontrivial, sinceagents need to perform well to send each other useful signals, but also need tosense others' signals to perform well. In this work, we develop several keylearning techniques for training policies with stigmergic interactions, wheresuch a circular dependency is present. By relying on clever curriculum learningdesign, action filtering, and the introduction of non-learning agents toincrease the agent density at training time at low computational cost, wedevelop a minimal learning framework that leads to the stable training ofefficient stigmergic policies. We present simulation results which demonstratethat our learned policy outperforms existing state-of-the-art MAF algorithms ina set of experiments that vary team size, number and placement of resources,and key environmental dynamics not seen at training time.

ForMIC: Foraging via Multiagent RL with Implicit Communication