  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.
