Determination of Ship Collision Avoidance Path using Deep Deterministic Policy Gradient Algorithm

Kim, Dong-Ham; Lee, Sung Uk; Nam, Jong-Ho; Furukawa, Yoshitaka

doi:10.3744/snak.2019.56.1.058

Cited by 8 publications

(1 citation statement)

References 6 publications

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“…Xu et al 22 used the DDPG method to learn collision avoidance behavior in the continuous state and action space, and obtained an effective collision avoidance strategy. Kim et al 23 also applied the DDPG algorithm to carry out ship collision avoidance policies, using the relative motion parameters between the own ship and the target ships (other ships in the area except the own ship), and the distance between the own ship and the target track. The state-space simplifies the complexity of learning tasks.…”

Section: Introductionmentioning

confidence: 99%

Research on autonomous collision avoidance of merchant ship based on inverse reinforcement learning

Zheng

Xie

Chu

et al. 2020

International Journal of Advanced Robotic Systems

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To learn the optimal collision avoidance policy of merchant ships controlled by human experts, a finite-state Markov decision process model for ship collision avoidance is proposed based on the analysis of collision avoidance mechanism, and an inverse reinforcement learning (IRL) method based on cross entropy and projection is proposed to obtain the optimal policy from expert’s demonstrations. Collision avoidance simulations in different ship encounters are conducted and the results show that the policy obtained by the proposed IRL has a good inversion effect on two kinds of human experts, which indicate that the proposed method can effectively learn the policy of human experts for ship collision avoidance.

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Section: Introductionmentioning

confidence: 99%