Visual Navigation with Asynchronous Proximal Policy Optimization in Artificial Agents. (15th October 2020)

• Record Type: Journal Article
• Authors: Zeng, Fanyu; Wang, Chen
• Other Names: Wang Weitian Academic Editor.
• Is Part Of: Journal of robotics. Volume 2020(2020)
• Publication Date: 2020-10-15
• Subjects: Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1155/2020/8702962
• Languages: English
• ISSNs: 1687-9600

Abstract

Abstract : Vanilla policy gradient methods suffer from high variance, leading to unstable policies during training, where the policy's performance fluctuates drastically between iterations. To address this issue, we analyze the policy optimization process of the navigation method based on deep reinforcement learning (DRL) that uses asynchronous gradient descent for optimization. A variant navigation (asynchronous proximal policy optimization navigation, appoNav ) is presented that can guarantee the policy monotonic improvement during the process of policy optimization. Our experiments are tested in DeepMind Lab, and the experimental results show that the artificial agents with appoNav perform better than the compared algorithm.