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

Author:

Zheng Mao1ORCID,Xie Shuo2ORCID,Chu Xiumin1,Zhu Tianquan13,Tian Guohao13

Affiliation:

1. National Engineering Research Center for Water Transportation Safety, Wuhan University of Technology, Wuhan, Hubei Province, People’s Republic of China

2. China Classification Society, Beijing, People’s Republic of China

3. School of Energy and Power Engineering, Wuhan University of Technology, Wuhan, Hubei Province, People’s Republic of China

Abstract

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.

Funder

the Fundamental Research Funds for the Central Universities

national science and technology infrastructure program

2. the Development of Ship Situation Intelligent Awareness System

ministry of industry and information technology of the people's republic of china

national key research and development program of china

national natural science foundation of china

Publisher

SAGE Publications

Subject

Artificial Intelligence,Computer Science Applications,Software

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