Interactive imitation learning for spacecraft path-planning in binary asteroid systems

Parmar, Kanak; Guzzetti, Davide

doi:10.1016/j.asr.2021.04.023

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…In maritime navigation, to enable autonomous navigation of ships in unmanned conditions, a comprehensive ship motion control system needs to be designed [15][16][17]. The autonomous ship running system consists of four components: control system, guidance system, navigation system, and path-planning.…”

Section: Fermat Curve Path-planningmentioning

confidence: 99%

Fermat Curve Path Planning Method for Ship Trajectory Tracking

2024

IJCAI

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Section: Fermat Curve Path-planningmentioning

confidence: 99%

Fermat Curve Path Planning Method for Ship Trajectory Tracking

2024

IJCAI

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“…In recent years, deep reinforcement learning (DRL) has gradually become a research focus and the development trend in the field of artificial intelligence and was selected by MIT as one of the 10 breakthrough technologies in 2017 [1]. Various deep learning methods such as Dagger [2], Deep Q-learning [3], DRL [4] and DNQ [5] are publicized. Among them, the Deep Deterministic Policy Gradient (DDPG) is widely used because of its excellent ability to observe and execute actions instantly in terms of individual intelligence [6], such as for the robotic arms to achieve high precise actions [7], for the Autonomous Underwater Vehicles (AUVs) to patrol intelligently [8].…”

Section: Introductionmentioning

confidence: 99%

An Interactive Self-Learning Game and Evolutionary Approach Based on Non-Cooperative Equilibrium

Zhao

Zhang

et al. 2021

Electronics

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Most current studies on multi-agent evolution based on deep learning take a cooperative equilibrium strategy, while interactive self-learning is not always considered. An interactive self-learning game and evolution method based on non-cooperative equilibrium (ISGE-NCE) is proposed to take the benefits of both game theory and interactive learning for multi-agent confrontation evolution. A generative adversarial network (GAN) is designed combining with multi-agent interactive self-learning, and the non-cooperative equilibrium strategy is well adopted within the framework of interactive self-learning, aiming for high evolution efficiency and interest. For assessment, three typical multi-agent confrontation experiments are designed and conducted. The results show that, first, in terms of training speed, the ISGE-NCE produces a training convergence rate of at least 46.3% higher than that of the method without considering interactive self-learning. Second, the evolution rate of the interference and detection agents reaches 60% and 80%, respectively, after training by using our method. In the three different experiment scenarios, compared with the DDPG, our ISGE-NCE method improves the multi-agent evolution effectiveness by 43.4%, 50%, and 20%, respectively, with low training costs. The performances demonstrate the significant superiority of our ISGE-NCE method in swarm intelligence.

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Disturbance observer-based nonfragile fuzzy tracking control of a spacecraft

Han

Kim

2023

Advances in Space Research

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Interactive imitation learning for spacecraft path-planning in binary asteroid systems

Cited by 8 publications

References 15 publications

Fermat Curve Path Planning Method for Ship Trajectory Tracking

Fermat Curve Path Planning Method for Ship Trajectory Tracking

An Interactive Self-Learning Game and Evolutionary Approach Based on Non-Cooperative Equilibrium

Disturbance observer-based nonfragile fuzzy tracking control of a spacecraft

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