[1]王丙琛,司怀伟,谭国真.基于深度强化学习的自动驾驶车控制算法研究[J].郑州大学学报(工学版),2020,41(04):41-45.[doi:10.13705/j.issn.1671-6833.2020.04.002]
 Wang Bingchen,Si Huaiwei,Tan Guozhen.Research on Autopilot Control Algorithms Based on Deep Reinforcement Learning[J].Journal of Zhengzhou University (Engineering Science),2020,41(04):41-45.[doi:10.13705/j.issn.1671-6833.2020.04.002]
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基于深度强化学习的自动驾驶车控制算法研究()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
41卷
期数:
2020年04期
页码:
41-45
栏目:
出版日期:
2020-08-12

文章信息/Info

Title:
Research on Autopilot Control Algorithms Based on Deep Reinforcement Learning
作者:
王丙琛司怀伟谭国真
大连理工大学计算机科学与技术学院
Author(s):
Wang BingchenSi HuaiweiTan Guozhen
School of Computer Science and Technology, Dalian University of Technology
关键词:
神经网络' target="_blank" rel="external">">神经网络强化学习自动驾驶DDPG算法actor-critic网络LSTM
Keywords:
neural networksreinforcement learningautonomous drivingDDPG algorithmactor-critic networkLSTM
DOI:
10.13705/j.issn.1671-6833.2020.04.002
文献标志码:
A
摘要:
自动驾驶是人工智能研究的重要领域之一.本文提出了一种基于深度强化学习的自动驾驶策略学习算法.采用基于DDPG的强化学习算法进行模型的在线训练,使用真实的人类驾驶数据对Actor网络进行预训练,避免了在强化学习的初始阶段智能体从零学习的过程,加快了模型的收敛速度.同时为了让智能体更好的做出决策,学习对未来状况的预判,在Actor网络中加入LSTM预测机制,增强了模型的稳定性和泛化能力.通过与原始DDPG算法进行比较,本文所提算法的训练时间大大缩短,收敛速度加快,提升了模型的稳定性
Abstract:
Auto-driving is one of the important fields of artificial intelligence research. This paper proposes a learning algorithm of auto-driving strategy based on deep reinforcement learning. It uses DDPG-based reinforcement learning algorithm to train the model online, and uses real human driving data to pre-train the Actor network. It avoids the process of agent zero-learning in the initial stage of reinforcement learning, and speeds up the convergence of the model . At the same time, LSTM prediction mechanism is added to the Actor network to improve the stability and generalization ability of the model. Compared with the original DDPG algorithm, the training time of the proposed method is greatly shortened, the convergence speed is accelerated and the stability of the model is improved

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更新日期/Last Update: 2020-10-06