TY - GEN
T1 - Coordination in Collaborative Work by Deep Reinforcement Learning with Various State Descriptions
AU - Miyashita, Yuki
AU - Sugawara, Toshiharu
N1 - Funding Information:
This work was partly supported by JSPS KAKENHI Grant Number 17KT0044.
Funding Information:
supported by JSPS KAKENHI Grant
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Cooperation and coordination are sophisticated behaviors and are still major issues in studies on multi-agent systems because how to cooperate and coordinate depends on not only environmental characteristics but also the behaviors/strategies that closely affect each other. On the other hand, recently using the multi-agent deep reinforcement learning (MADRL) has received much attention because of the possibility of learning and facilitating their coordinated behaviors. However, the characteristics of socially learned coordination structures have been not sufficiently clarified. In this paper, by focusing on the MADRL in which each agent has its own deep Q-networks (DQNs), we show that the different types of input to the network lead to various coordination structures, using the pickup and floor laying problem, which is an abstract form related to our target problem. We also indicate that the generated coordination structures affect the entire performance of multi-agent systems.
AB - Cooperation and coordination are sophisticated behaviors and are still major issues in studies on multi-agent systems because how to cooperate and coordinate depends on not only environmental characteristics but also the behaviors/strategies that closely affect each other. On the other hand, recently using the multi-agent deep reinforcement learning (MADRL) has received much attention because of the possibility of learning and facilitating their coordinated behaviors. However, the characteristics of socially learned coordination structures have been not sufficiently clarified. In this paper, by focusing on the MADRL in which each agent has its own deep Q-networks (DQNs), we show that the different types of input to the network lead to various coordination structures, using the pickup and floor laying problem, which is an abstract form related to our target problem. We also indicate that the generated coordination structures affect the entire performance of multi-agent systems.
KW - Cooperation
KW - Coordination
KW - Deep Q networks
KW - Divisional cooperation
KW - Multi-agent deep reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85076503622&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076503622&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-33792-6_40
DO - 10.1007/978-3-030-33792-6_40
M3 - Conference contribution
AN - SCOPUS:85076503622
SN - 9783030337919
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 550
EP - 558
BT - PRIMA 2019
A2 - Baldoni, Matteo
A2 - Dastani, Mehdi
A2 - Liao, Beishui
A2 - Sakurai, Yuko
A2 - Zalila Wenkstern, Rym
PB - Springer
T2 - 22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2019
Y2 - 28 October 2019 through 31 October 2019
ER -