TY - GEN
T1 - Multi-Agent Pattern Formation with Deep Reinforcement Learning
AU - Diallo, Elhadji Amadou Oury
AU - Sugawara, Toshiharu
N1 - Funding Information:
∗Partly supported by JSPS KAKENHI Grant No. 17KT0044. Copyright ©c 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Publisher Copyright:
© 2020 The Twenty-Fifth AAAI/SIGAI Doctoral Consortium (AAAI-20). All Rights Reserved.
PY - 2020
Y1 - 2020
N2 - We propose a decentralized multi-agent deep reinforcement learning architecture to investigate pattern formation under the local information provided by the agents' sensors. It consists of tasking a large number of homogeneous agents to move to a set of specified goal locations, addressing both the assignment and trajectory planning sub-problems concurrently. We then show that agents trained on random patterns can organize themselves into very complex shapes.
AB - We propose a decentralized multi-agent deep reinforcement learning architecture to investigate pattern formation under the local information provided by the agents' sensors. It consists of tasking a large number of homogeneous agents to move to a set of specified goal locations, addressing both the assignment and trajectory planning sub-problems concurrently. We then show that agents trained on random patterns can organize themselves into very complex shapes.
UR - http://www.scopus.com/inward/record.url?scp=85093838159&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85093838159&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85093838159
T3 - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
SP - 13779
EP - 13780
BT - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
PB - AAAI Press
T2 - 34th AAAI Conference on Artificial Intelligence, AAAI 2020
Y2 - 7 February 2020 through 12 February 2020
ER -