Visual Navigation with Asynchronous Proximal Policy Optimization in Artificial Agents

Joint Authors

Zeng, Fanyu
Wang, Chen

Source

Journal of Robotics

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-7, 7 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-10-15

Country of Publication

Egypt

No. of Pages

7

Main Subjects

Mechanical Engineering

Abstract EN

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.

American Psychological Association (APA)

Zeng, Fanyu& Wang, Chen. 2020. Visual Navigation with Asynchronous Proximal Policy Optimization in Artificial Agents. Journal of Robotics،Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1190254

Modern Language Association (MLA)

Zeng, Fanyu& Wang, Chen. Visual Navigation with Asynchronous Proximal Policy Optimization in Artificial Agents. Journal of Robotics No. 2020 (2020), pp.1-7.
https://search.emarefa.net/detail/BIM-1190254

American Medical Association (AMA)

Zeng, Fanyu& Wang, Chen. Visual Navigation with Asynchronous Proximal Policy Optimization in Artificial Agents. Journal of Robotics. 2020. Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1190254

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1190254