Verifying the effectiveness of transferring different action choices when applying reinforcement learning to action planning issues for AGV

Abstract

Reinforcement learning is often used to solve AGV problems, such as the package transport problem. Roulette selection and epsilon-greedy are typical strategies for action selection in reinforcement learning. In this study, we examine the transport efficiency of AGV for the two types of action selection and verify a method that combines the two types of action selection.