Visual navigation with Actor-Critic deep reinforcement learning | |
Kun Shao1,2; Dongbin Zhao1,2; Yuanheng Zhu1,2; Qichao Zhang1,2 | |
2018-03 | |
会议日期 | 2018-01 |
会议地点 | Rio, Brazil |
英文摘要 | 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 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/23365] |
专题 | 复杂系统管理与控制国家重点实验室_深度强化学习 |
通讯作者 | Yuanheng Zhu |
作者单位 | 1.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 |
推荐引用方式 GB/T 7714 | Kun Shao,Dongbin Zhao,Yuanheng Zhu,et al. Visual navigation with Actor-Critic deep reinforcement learning[C]. 见:. Rio, Brazil. 2018-01. |
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