TY - GEN
T1 - MADDPG-Based Distributed Cooperative Search Strategy for Heterogeneous Agents System
AU - Wang, Ruizhe
AU - Xia, Yuanqing
AU - Wei, Yiran
AU - Pan, Zhenhua
AU - Li, Jie
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - The limited communication among agents is recognized as a significant constraint. It hinders and delays the collaborative exploration and exploitation of unknown environments. To tackle the challenge of cooperative search in communication-denied environments for agent swarms. We present a feature-based multi-agent reinforcement learning (MARL) framework. Firstly, we categorize agents into distinct roles based on their diverse characteristics and introduce a communication-complementary framework for multi-agent cooperation to maximize the benefits of individual agent characteristics. Secondly, we present a detailed introduction to the feature-based MADDPG algorithm, which effectively balances individual and collective benefits through a reward function. Finally, we assess the effectiveness of the proposed method through multiple simulations, showcasing its ability to effectively coordinate diverse agents.
AB - The limited communication among agents is recognized as a significant constraint. It hinders and delays the collaborative exploration and exploitation of unknown environments. To tackle the challenge of cooperative search in communication-denied environments for agent swarms. We present a feature-based multi-agent reinforcement learning (MARL) framework. Firstly, we categorize agents into distinct roles based on their diverse characteristics and introduce a communication-complementary framework for multi-agent cooperation to maximize the benefits of individual agent characteristics. Secondly, we present a detailed introduction to the feature-based MADDPG algorithm, which effectively balances individual and collective benefits through a reward function. Finally, we assess the effectiveness of the proposed method through multiple simulations, showcasing its ability to effectively coordinate diverse agents.
KW - Cooperative search
KW - heterogeneous multi-agent system
KW - multi-agent reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85199365143&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-3332-3_26
DO - 10.1007/978-981-97-3332-3_26
M3 - Conference contribution
AN - SCOPUS:85199365143
SN - 9789819733316
T3 - Lecture Notes in Electrical Engineering
SP - 292
EP - 305
BT - Proceedings of 2023 7th Chinese Conference on Swarm Intelligence and Cooperative Control - Swarm Perception and Navigation Technologies
A2 - Yu, Jianglong
A2 - Li, Qingdong
A2 - Liu, Yumeng
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023
Y2 - 24 November 2023 through 27 November 2023
ER -