@inproceedings{dbff3fbde8f14419a4d8d00f318a7dc0,
title = "Learning to communicate: A machine learning framework for heterogeneous multi-agent robotic systems",
abstract = "We present a machine learning framework for multi-agent systems to learn both the optimal policy for maximizing the rewards and the encoding of the high dimensional visual observation. The encoding is useful for sharing local visual observations with other agents under communication resource constraints. The actor-encoder encodes the raw images and chooses an action based on local observations and messages sent by the other agents. The machine learning agent generates not only an actuator command to the physical device, but also a communication message to the other agents. We formulate a reinforcement learning problem, which extends the action space to consider the communication action as well. The feasibility of the reinforcement learning framework is demonstrated using a 3D simulation environment with two collaborating agents. The environment provides realistic visual observations to be used and shared between the two agents.",
author = "Yoon, {Hyung Jin} and Huaiyu Chen and Kehan Long and Heling Zhang and Aditya Gahlawat and Donghwan Lee and Naira Hovakimyan",
note = "Funding Information: This material is based upon work supported by the National Science Foundation under National Robotics Initiative grant #1830639 and Air Force Office of Scientific Research grant #FA9550-18-1-0269. Publisher Copyright: {\textcopyright} 2019, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.; AIAA Scitech Forum, 2019 ; Conference date: 07-01-2019 Through 11-01-2019",
year = "2019",
doi = "10.2514/6.2019-1456",
language = "English (US)",
isbn = "9781624105784",
series = "AIAA Scitech 2019 Forum",
publisher = "American Institute of Aeronautics and Astronautics Inc, AIAA",
booktitle = "AIAA Scitech 2019 Forum",
}