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Research on autonomous collision avoidance of merchant ship based on inverse reinforcement learning
| Content Provider | SAGE Publishing |
|---|---|
| Author | Zheng, Mao Xie, Shuo Chu, Xiumin Zhu, Tianquan Tian, Guohao |
| Copyright Year | 2020 |
| 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. |
| Related Links | https://journals.sagepub.com/doi/pdf/10.1177/1729881420969081?download=true |
| ISSN | 17298806 |
| Issue Number | 6 |
| Volume Number | 17 |
| Journal | International Journal of Advanced Robotic Systems (ARX) |
| e-ISSN | 17298814 |
| DOI | 10.1177/1729881420969081 |
| Language | English |
| Publisher | Sage Publications UK |
| Publisher Date | 2020-11-12 |
| Publisher Place | London |
| Access Restriction | Open |
| Rights Holder | © The Author(s) 2020 |
| Subject Keyword | merchant ship collision avoidance cross entropy projection Inverse reinforcement learning |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Computer Science Applications Software |
