CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 深度强化学习
Visual navigation with Actor-Critic deep reinforcement learning
Kun Shao1,2; Dongbin Zhao1,2; Yuanheng Zhu1,2; Qichao Zhang1,2
2018-03
Conference NameIEEE International Joint Conference on Neural Networks
Conference Date2018-01
Conference PlaceRio, Brazil
Abstract

Visual navigation in complex environments is crucial for intelligent agents. In this paper, we propose an efficient deep reinforcement learning (DRL) method to tackle visual navigation tasks. We present the synchronous advantage actor-critic (A2C) with generalized advantage estimator (GAE) algorithm. The A2C enables agents to learn from multiple processes, which significantly reduces the training time. The GAE used to estimate the advantage function improves the policy gradient estimates. We focus on visual navigation tasks in ViZDoom, and train agents in two health gathering scenarios. The experimental results show this method successfully teaches our agents to navigate in these scenarios. The A2C with GAE agent reaches the highest score
in the first task, and a competitive score in the second task. In
addition, this agent has better average scores and lower variances
in both tasks.

Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23365
Collection复杂系统管理与控制国家重点实验室_深度强化学习
Corresponding AuthorYuanheng Zhu
Affiliation1.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Kun Shao,Dongbin Zhao,Yuanheng Zhu,et al. Visual navigation with Actor-Critic deep reinforcement learning[C],2018.
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