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Human and Multi-Agent collaboration in a human-MARL teaming framework
by
Schuh, Andea
, Saga Kurandwa
, Navidi, Neda
, Szrftgr, Gregry
, Robt, Vincent
, Chabo, Francoi
, Lutigma, Iv
in
Collaboration
/ Decoupling
/ Innovations
/ Machine learning
/ Multiagent systems
/ Real time
/ Seats
/ Teaching methods
/ Unmanned aerial vehicles
2021
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Human and Multi-Agent collaboration in a human-MARL teaming framework
by
Schuh, Andea
, Saga Kurandwa
, Navidi, Neda
, Szrftgr, Gregry
, Robt, Vincent
, Chabo, Francoi
, Lutigma, Iv
in
Collaboration
/ Decoupling
/ Innovations
/ Machine learning
/ Multiagent systems
/ Real time
/ Seats
/ Teaching methods
/ Unmanned aerial vehicles
2021
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Do you wish to request the book?
Human and Multi-Agent collaboration in a human-MARL teaming framework
by
Schuh, Andea
, Saga Kurandwa
, Navidi, Neda
, Szrftgr, Gregry
, Robt, Vincent
, Chabo, Francoi
, Lutigma, Iv
in
Collaboration
/ Decoupling
/ Innovations
/ Machine learning
/ Multiagent systems
/ Real time
/ Seats
/ Teaching methods
/ Unmanned aerial vehicles
2021
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Human and Multi-Agent collaboration in a human-MARL teaming framework
Paper
Human and Multi-Agent collaboration in a human-MARL teaming framework
2021
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Overview
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.
Publisher
Cornell University Library, arXiv.org
Subject
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