End-to-end self-driving policy based on the deep deterministic policy gradient algorithm considering the state distribution

doi:10.16511/j.cnki.qhdxxb.2020.22.038

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Abstract End-to-end control is one approach for self-driving. However, end-to-end self-driving methods based on reinforcement learning have troubles dealing with widely varying driving scenarios. The learning algorithm cannot easily determine the random decline velocity during training for complex scenarios. If the velocity is too fast, the algorithm will not obtain a reasonable policy for new scenarios, while if the velocity is too slow, the algorithm will not converge fast. A random policy and experience replay method based on the state distribution is developed here to improve the random decline velocity selection. The various random process parameters are selected based on the distance between the present state and the saved states. In addition, the replay probability of various scenes that occur less frequently is also increased. Simulations show that the algorithm has sufficient exploration ability in the later stage of the training when faced with scenes having very different situations from the situations in the early stages, which improves the lane keeping ability of the end-to-end self-driving approach based on the deep deterministic policy gradient (DDPG) in new situations.

Keywords end-to-end self-driving reinforcement learning random policy experience replay

Issue Date: 21 August 2021

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	WANG Tinghan
	LUO Yugong
	LIU Jinxin
	LI Keqiang

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WANG Tinghan,LUO Yugong,LIU Jinxin, et al. End-to-end self-driving policy based on the deep deterministic policy gradient algorithm considering the state distribution[J]. Journal of Tsinghua University(Science and Technology), 2021, 61(9): 881-888.

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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2020.22.038 OR http://jst.tsinghuajournals.com/EN/Y2021/V61/I9/881

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