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	Continuous Reinforcement Learning From Human Demonstrations With Integrated Experience Replay for Autonomous Driving
	Sixiang Zuo; Yongsheng Ou; Xiaorui Zhu; Zhiyang Wang
	2017
会议地点	Maccau SAR, China
英文摘要	As a promising subfield of machine learning, Reinforcement Learning (RL) has drawn increasing attention among the academia as well as the public. However, the practical application of RL is still restricted by a variety of reasons. The two most significant challenges of RL are the large exploration domain and the difficulty to converge. Integrating RL with human expertise is technically an interesting way to accelerate the exploration and increase the stability. In this work, we propose a continuous reinforcement learning method which integrates Deep Deterministic Policy Gradient (DDPG) with human demonstrations. The proposed method uses a combined loss function for updating the actor and critic networks. In addition, the experience replay buffer is also drawn from different transition data samples to make the learning more stable. The proposed method is tested with a popular RL task, i.e. the autonomous driving, by simulations with TORCS environment. Experimental results not only show the effectiveness of our method in improving the learning stability, but also manifest the potential capability of our method in mastering human preferences.
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/11888]
专题	深圳先进技术研究院_集成所
作者单位	2017
推荐引用方式 GB/T 7714	Sixiang Zuo,Yongsheng Ou,Xiaorui Zhu,et al. Continuous Reinforcement Learning From Human Demonstrations With Integrated Experience Replay for Autonomous Driving[C]. 见:. Maccau SAR, China.

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