TY - GEN
T1 - Analysis of Coordination Structures of Partially Observing Cooperative Agents by Multi-agent Deep Q-Learning
AU - Smith, Ken
AU - Miyashita, Yuki
AU - Sugawara, Toshiharu
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - We compare the coordination structures of agents using different types of inputs for their deep Q-networks (DQNs) by having agents play a distributed task execution game. The efficiency and performance of many multi-agent systems can be significantly affected by the coordination structures formed by agents. One important factor that may affect these structures is the information provided to an agent’s DQN. In this study, we analyze the differences in coordination structures in an environment involving walls to obstruct visibility and movement. Additionally, we introduce a new DQN input, which performs better than past inputs in a dynamic setting. Experimental results show that agents with their absolute locations in their DQN input indicate a granular level of labor division in some settings, and that the consistency of the starting locations of agents significantly affects the coordination structures and performances of agents.
AB - We compare the coordination structures of agents using different types of inputs for their deep Q-networks (DQNs) by having agents play a distributed task execution game. The efficiency and performance of many multi-agent systems can be significantly affected by the coordination structures formed by agents. One important factor that may affect these structures is the information provided to an agent’s DQN. In this study, we analyze the differences in coordination structures in an environment involving walls to obstruct visibility and movement. Additionally, we introduce a new DQN input, which performs better than past inputs in a dynamic setting. Experimental results show that agents with their absolute locations in their DQN input indicate a granular level of labor division in some settings, and that the consistency of the starting locations of agents significantly affects the coordination structures and performances of agents.
UR - http://www.scopus.com/inward/record.url?scp=85102772933&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-69322-0_10
DO - 10.1007/978-3-030-69322-0_10
M3 - Conference contribution
AN - SCOPUS:85102772933
SN - 9783030693213
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 150
EP - 164
BT - PRIMA 2020
A2 - Uchiya, Takahiro
A2 - Bai, Quan
A2 - Marsá Maestre, Iván
PB - Springer Science and Business Media Deutschland GmbH
T2 - 23rd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2020
Y2 - 18 November 2020 through 20 November 2020
ER -