论文标题
人类和多代理的合作在人类摩尔队的框架中
Human and Multi-Agent collaboration in a human-MARL teaming framework
论文作者
论文摘要
强化学习可以通过代理从观察结果学习，获得的奖励以及代理之间的内部互动提供有效的结果。这项研究提出了一个新的开源MARL框架，称为Cogment，以有效利用人类和代理人的相互作用作为学习来源。我们通过使用由RL代理商驱动的无人飞行器使用设计的实时环境来证明这些创新，并与人合作。这项研究的结果表明，拟议的合作范式和开源框架可显着降低人类努力和勘探成本。
Reinforcement learning provides effective results with agents learning from their observations, received rewards, and internal interactions between agents. This study proposes a new open-source MARL framework, called COGMENT, to efficiently leverage human and agent interactions as a source of learning. We demonstrate these innovations by using a designed real-time environment with unmanned aerial vehicles driven by RL agents, collaborating with a human. The results of this study show that the proposed collaborative paradigm and the open-source framework leads to significant reductions in both human effort and exploration costs.
