Integrated reinforcement and imitation learning for tower crane lift path planning

Abstract

Reinforcement learning (RL) has emerged as a promising solution method for crane-lift path planning. However, designing appropriate reward functions for tower crane (TC) operations remains particularly challenging. Poor design of reward functions can lead to non-executable lifting paths. This paper presents a framework combining imitation learning (IL) and RL to address the challenge. The framework comprises three steps: (1) designing a virtual environment consisting of construction site models and a TC model, (2) collecting expert demonstrations through virtual reality (VR) and pretraining through behavioral cloning (BC), and (3) refining the BC policies via generative adversarial imitation learning (GAIL) and proximal policy optimization (PPO). Using the paths generated by a PPO model as the baseline, the proposed BC + PPO + GAIL model exhibited better performance in both blind and nonblind lifting scenarios. This framework has been proven to generate realistic lifting paths mirroring crane operator behavior while ensuring efficiency and safety.