Random Network Distillation Based Deep Reinforcement Learning for AGV Path Planning

arXiv:2404.12594v1 Announce Type: cross
Abstract: With the flourishing development of intelligent warehousing systems, the technology of Automated Guided Vehicle (AGV) has experienced rapid growth. Within intelligent warehousing environments, AGV is required to safely and rapidly plan an optimal path in complex and dynamic environments. Most research has studied deep reinforcement learning to address this challenge. However, in the environments with sparse extrinsic rewards, these algorithms often converge slowly, learn inefficiently or fail to reach the target. Random Network Distillation (RND), …

arxiv cs.ai cs.lg cs.ro distillation network path planning random reinforcement reinforcement learning type