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

• URL: http://arxiv.org/abs/2006.07301v2
• Date: Mon, 1 Mar 2021 21:24:19 GMT
• Title: Human and Multi-Agent collaboration in a human-MARL teaming framework
• Authors: Neda Navidi, Francoi Chabo, Saga Kurandwa, Iv Lutigma, Vincent Robt, Gregry Szrftgr, Andea Schuh
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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