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Authors: Aniket Gutpa 1 and Raghava Nallanthighal 2

Affiliations: 1 Department of Electrical Engineering, Delhi Technological University, New Delhi, India ; 2 Department of Electronics and Communication Engineering, Delhi Technological University, New Delhi, India

Keyword(s): Multi-agent Systems, Swarm Robotics, Formation Control, Policy Gradient Methods.

Abstract: Multi-agent formation control has been a much-researched topic and while several methods from control theory exist, they require astute expertise to tune properly which is highly resource-intensive and often fails to adapt properly to slight changes in the environment. This paper presents an end-to-end decentralized approach towards multi-agent formation control with the information available from onboard sensors by using a Deep Reinforcement learning framework. The proposed method directly utilizes the raw sensor readings to calculate the agent’s movement velocity using a Deep Neural Network. The approach utilizes Policy gradient methods to generalize efficiently on various simulation scenarios and is trained over a large number of agents. We validate the performance of the learned policy using numerous simulated scenarios and a comprehensive evaluation. Finally, the performance of the learned policy is demonstrated in new scenarios with non-cooperative agents that were not introduc ed during the training process. (More)

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Paper citation in several formats:
Gutpa, A. and Nallanthighal, R. (2021). Decentralized Multi-agent Formation Control via Deep Reinforcement Learning. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-484-8; ISSN 2184-433X, SciTePress, pages 289-295. DOI: 10.5220/0010241302890295

@conference{icaart21,
author={Aniket Gutpa. and Raghava Nallanthighal.},
title={Decentralized Multi-agent Formation Control via Deep Reinforcement Learning},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2021},
pages={289-295},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010241302890295},
isbn={978-989-758-484-8},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Decentralized Multi-agent Formation Control via Deep Reinforcement Learning
SN - 978-989-758-484-8
IS - 2184-433X
AU - Gutpa, A.
AU - Nallanthighal, R.
PY - 2021
SP - 289
EP - 295
DO - 10.5220/0010241302890295
PB - SciTePress

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