Two-Stage Reinforcement Learning with Residual Value Functions for Autonomous Forklift Control

Abstract

This study proposes a two-stage reinforcement learning method with residual value functions for autonomous forklifts. Automation in diverse environments is essential for forklifts due to their versatility and widespread use. However, learning from scratch in diverse environments is highly costly. To improve efficiency, we divide the forklift control task into common and environment-specific components. The common components are learned in the first stage, while the environment-specific components are efficiently learned in the second stage using residual reinforcement learning. This task division enables reuse of the learning outcomes from the common components. The evaluation experiment demonstrates that our method outperforms conventional methods in terms of success rate.