TY - GEN
T1 - A Multi-Agent Deep Reinforcement Learning Framework for the Stable Landing of a Flexibly Connected Three-Node Space Probe
AU - Fu, Kang
AU - Zhao, Qingjie
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Exploring asteroids in the solar system is crucial for human survival and development in the future, since the asteroids may contain important clues about the origin of life which can bring important inspiration to humanity. However, due to the weak gravitational force of asteroids, as well as the limited knowledge about them, it is challenging for a space probe to land on these asteroids' surface steadily. To this end, we propose a flexible connected three-node probe to significantly boost the landing stability, in which each node is relatively independent in behaviors but constrained with each other. As a multi-agent system with constraints, a cooperative behavior planning and decision-making method is very necessary. In this paper, we propose a multi-agent deep reinforcement learning (MADRL) framework for addressing the stable landing of a flexibly connected three-node space probe. In order to overcome the uncertainty of the environment, a training paradigm of centralized training decentralized execution (CTDE) is adopted, where the information exchange between agents is taken into account. Moreover, we integrate gated recurrent unit (GRU) modules into actor and critic networks to preserve the historical information, so that the learned strategy can apply to uncertain asteroid environments and implement stable probe landing. Experimental results demonstrate that the proposed method outperforms other reinforcement learning-based methods in terms of convergence and stability.
AB - Exploring asteroids in the solar system is crucial for human survival and development in the future, since the asteroids may contain important clues about the origin of life which can bring important inspiration to humanity. However, due to the weak gravitational force of asteroids, as well as the limited knowledge about them, it is challenging for a space probe to land on these asteroids' surface steadily. To this end, we propose a flexible connected three-node probe to significantly boost the landing stability, in which each node is relatively independent in behaviors but constrained with each other. As a multi-agent system with constraints, a cooperative behavior planning and decision-making method is very necessary. In this paper, we propose a multi-agent deep reinforcement learning (MADRL) framework for addressing the stable landing of a flexibly connected three-node space probe. In order to overcome the uncertainty of the environment, a training paradigm of centralized training decentralized execution (CTDE) is adopted, where the information exchange between agents is taken into account. Moreover, we integrate gated recurrent unit (GRU) modules into actor and critic networks to preserve the historical information, so that the learned strategy can apply to uncertain asteroid environments and implement stable probe landing. Experimental results demonstrate that the proposed method outperforms other reinforcement learning-based methods in terms of convergence and stability.
KW - centralized training decentralized execution
KW - gated recurrent unit
KW - Multi-agent deep reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85204950089&partnerID=8YFLogxK
U2 - 10.1109/IJCNN60899.2024.10650175
DO - 10.1109/IJCNN60899.2024.10650175
M3 - Conference contribution
AN - SCOPUS:85204950089
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2024 International Joint Conference on Neural Networks, IJCNN 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 International Joint Conference on Neural Networks, IJCNN 2024
Y2 - 30 June 2024 through 5 July 2024
ER -