Implementing action mask in proximal policy optimization (PPO) algorithm

Cheng-Yen Tang

doi:10.1016/j.icte.2020.05.003

Implementing action mask in proximal policy optimization (PPO) algorithm

2020-09-01

34

OA

AI

DOI：10.1016/j.icte.2020.05.003

原文链接

查看原文

摘要

En 中文

The proximal policy optimization (PPO) algorithm is a promising algorithm in reinforcement learning. In this paper, we propose to add an action mask in the PPO algorithm. The mask indicates whether an action is valid or invalid for each state. Simulation results show that, when compared with the original version, the proposed algorithm yields much higher return with a moderate number of training steps. Therefore, it is useful and valuable to incorporate such a mask if applicable. (C) 2020 The Korean Institute of Communications and Information Sciences (KICS). Publishing services by Elsevier B.V.

Keyword:

PPO

Invalid action

Reinforcement learning

Implementing action mask in proximal policy optimization (PPO) algorithm

摘要

AI总结

期刊

机构

引用论文