Abstract
In UAV path planning tasks, classical algorithms can only solve the tasks where environment is knowable and can be changed into mathematical model. For the tasks with unknown environment information is difficult to solve. However, such tasks can be easily solved by human. In this paper, we first bulid a UAV path planning environment and train the agent with deep reinforcement learning as human demonstration. Then, we use inverse reinforcement learning algorithm and processed human demonstrations to learn human policy. Finally, we test the trained policy in path planning tasks to verify the feasibility.
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Acknowledgement
This work was supported by the Science and Technology Innovation 2030-Key Project of “New Generation Artificial Intelligence” under Grant 2020AAA0108200, the National Natural Science Foundation of China under Grants 62103023,61922008, 61973013, 61873011 and 62103016 the Innovation Zone Project under Grant 18-163-00-TS-001-001-34, the National Defense Project under 201-CXCY-A01-08-00-01, the Foundation Strengthening Program Technology Field Fund under Grant 2019-JCJQ-JJ-243, the Defense Industrial Technology Development Program under Grant JCKY2019601C106, the Young Elite Scientists Sponsorship Program by CAST under Grant 2021QNRC001, China National Postdoctoral Program for Innovative Talents under Grant BX20200034, and the China Postdoctoral Science Foundation under Grant 2020M680297, the Young Elite Scientists Sponsorship Program by CAST under Grant 2021QNRC001.
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Xu, Z., Dong, J., Hua, Y., Dong, X., Li, Q., Ren, Z. (2023). UAV Path Planning from Human Demonstrations Using Inverse Reinforcement Learning. In: Yan, L., Duan, H., Deng, Y. (eds) Advances in Guidance, Navigation and Control. ICGNC 2022. Lecture Notes in Electrical Engineering, vol 845. Springer, Singapore. https://doi.org/10.1007/978-981-19-6613-2_535
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DOI: https://doi.org/10.1007/978-981-19-6613-2_535
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