ForMIC：基于多智能体RL的隐式通信觅食
ForMIC: Foraging via Multiagent RL with Implicit Communication
摘要：
多智能体觅食（MAF）涉及分配一组智能体来搜索环境并从中提取资源。自然提供了几个高效觅食者的例子，其中觅食集体中的个体使用生物标记（例如，信息素）通过环境向其他人传达关键信息。在这项工作中，我们提出了ForMIC，一个分布式的强化学习MAF方法，赋予代理通过他们的共享环境的隐式通信能力。然而，学习有效的政策与stigmergic互动是非常不平凡的，因为代理需要表现良好，互相发送有用的信号，但也需要感觉到别人的信号表现良好。在这项工作中，我们开发了几种关键的学习技术，用于培训具有污名互动的政策，其中存在这种循环依赖性。通过依靠聪明的课程学习设计，动作过滤，并引入非学习代理，以增加代理密度在训练时，在低计算成本，我们开发了一个最小的学习框架，导致稳定的培训有效的stigmergic政策。我们提出的模拟结果表明，我们学到的政策优于现有的国家的最先进的MAF算法在一组实验，不同的团队规模，数量和资源的放置，以及关键的环境动态没有看到在训练时。
英文摘要：
Multi-agent foraging (MAF) involves distributing a team of agents to search an environment and extract resources from it. Nature provides several examples of highly effective foragers, where individuals within the foraging collective use biological markers (e.g., pheromones) to communicate critical information to others via the environment. In this work, we propose ForMIC, a distributed reinforcement learning MAF approach that endows agents with implicit communication abilities via their shared environment. However, learning efficient policies with stigmergic interactions is highly nontrivial, since agents need to perform well to send each other useful signals, but also need to sense others' signals to perform well. In this work, we develop several key learning techniques for training policies with stigmergic interactions, where such a circular dependency is present. By relying on clever curriculum learning design, action filtering, and the introduction of non-learning agents to increase the agent density at training time at low computational cost, we develop a minimal learning framework that leads to the stable training of efficient stigmergic policies. We present simulation results which demonstrate that our learned policy outperforms existing state-of-the-art MAF algorithms in a set of experiments that vary team size, number and placement of resources, and key environmental dynamics not seen at training time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
