Random Network Distillation Based Deep Reinforcement Learning for AGV Path Planning

TL;DR

This paper introduces a novel deep reinforcement learning approach using Random Network Distillation to improve AGV path planning in complex, continuous environments with sparse rewards, demonstrating faster and more efficient navigation.

Contribution

The paper proposes a new AGV path planning method combining RND with deep RL in continuous environments, addressing limitations of previous grid-based approaches.

Findings

Enhanced path planning speed in continuous environments

Effective handling of sparse reward scenarios

Improved AGV navigation efficiency

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), as an exploration enhancement, can effectively improve the performance of proximal policy optimization, especially enhancing the additional intrinsic rewards of the AGV agent which is in sparse reward environments. Moreover, most of the current research continues to use 2D grid mazes as experimental environments. These environments have insufficient complexity and limited action sets. To solve this limitation, we present simulation environments of AGV path planning with continuous actions and positions for AGVs, so that it can be close to realistic physical scenarios. Based on our experiments and comprehensive analysis of the proposed method, the results demonstrate that our proposed method enables AGV to more rapidly complete path planning tasks with continuous actions in our environments. A video of part of our experiments can be found at https://youtu.be/lwrY9YesGmw.