Title: A reinforcement learning algorithm for mobile robot path planning with dynamic Q-value adjustment

Authors: Chang Hua; Hao Zheng; Yiqin Bao

Addresses: Artificial Intelligence and Information Technology College, Nanjing University of Chinese Medicine, Nanjing 210023, China ' Information Engineering College, Nanjing XiaoZhuang University, Nanjing 211171, China ' Information Engineering College, Nanjing XiaoZhuang University, Nanjing 211171, China

Abstract: Path planning is essential for mobile robots to execute various tasks across different fields, including intelligent systems. It primarily focuses on the interaction between the agent and its environment, allowing the agent to maximise total reward by an optimal strategy. Many path-planning algorithms that are not agent-based struggle with effectively exploring entirely unknown environments. To address these issues, we propose the Adam deep Q-learning network (ADQN) to solve such problems. ADQN introduces an innovative approach to choosing action and reward functions, optimising Q-value updates dynamically based on temporal-difference error changes for enhanced model convergence and stability. Evaluated across four simulations in two maze environments of varying complexities, ADQN shows significant improvements: reduced steps, increased rewards, faster and stable loss convergence, and notably higher success rates compared to Munchausen reinforcement learning, prioritised experience replay-double duelling deep Q-networks, max-mean loss in deep Q-network algorithms in grid-based experiments.

Keywords: Adam deep Q-learning network; ADQN; path planning; agent; reward; selection strategy; Q-value.

DOI: 10.1504/IJSNET.2025.144555

International Journal of Sensor Networks, 2025 Vol.47 No.2, pp.113 - 125

Received: 09 Sep 2024
Accepted: 20 Sep 2024
Published online: 19 Feb 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article