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.
@article{arxiv.2006.07301,
title = {Human and Multi-Agent collaboration in a human-MARL teaming framework},
author = {Neda Navidi and Francoi Chabo and Saga Kurandwa and Iv Lutigma and Vincent Robt and Gregry Szrftgr and Andea Schuh},
journal= {arXiv preprint arXiv:2006.07301},
year = {2021}
}