Human and Multi-Agent collaboration in a human-MARL teaming framework

Neda Navidi; Francoi Chabo; Saga Kurandwa; Iv Lutigma; Vincent Robt; Gregry Szrftgr; Andea Schuh

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arXiv · 2006.07301

Human and Multi-Agent collaboration in a human-MARL teaming framework

Neda Navidi·Francoi Chabo·Saga Kurandwa·Iv Lutigma·Vincent Robt·Gregry Szrftgr·Andea Schuh

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Abstract

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.

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Neda Navidi, Francoi Chabo, Saga Kurandwa, Iv Lutigma, Vincent Robt, Gregry Szrftgr, Andea Schuh. 2020-06-12. Human and Multi-Agent collaboration in a human-MARL teaming framework. https://arxiv.org/abs/2006.07301

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