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Finally, the combination of Deep Q-Network (DQN) and RRT is achieved through the proposed DQN-RRT algorithm, which incorporates the structure and training method of DQN. Compared to the traditional RRT algorithm, the proposed algorithm balances planning autonomy, reduces search redundancy and efficient obstacle avoidance. Simulations are given to validate the performance optimization function of the proposed algorithm in RRT path planning. <\/jats:p>","DOI":"10.1142\/s2301385024420068","type":"journal-article","created":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T09:10:30Z","timestamp":1699521030000},"page":"447-456","source":"Crossref","is-referenced-by-count":6,"title":["A Novel Exploration Mechanism of RRT Guided by Deep <i>Q<\/i>-Network"],"prefix":"10.1142","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4057-2508","authenticated-orcid":false,"given":"Zhaoying","family":"Li","sequence":"first","affiliation":[{"name":"School of Astronautics, Beihang University, Beijing 100191, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4295-377X","authenticated-orcid":false,"given":"Jiaqi","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Astronautics, Beihang University, Beijing 100191, P. R. 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