Hybrid DQN-TD3 Reinforcement Learning for Autonomous Navigation in Dynamic Environments

📝 Original Info

• Title: Hybrid DQN-TD3 Reinforcement Learning for Autonomous Navigation in Dynamic Environments
• ArXiv ID: 2510.26646
• Date: 2025-10-30
• Authors: ** 정보 없음 (논문에 저자 정보가 제공되지 않음) **

📝 Abstract

This paper presents a hierarchical path-planning and control framework that combines a high-level Deep Q-Network (DQN) for discrete sub-goal selection with a low-level Twin Delayed Deep Deterministic Policy Gradient (TD3) controller for continuous actuation. The high-level module selects behaviors and sub-goals; the low-level module executes smooth velocity commands. We design a practical reward shaping scheme (direction, distance, obstacle avoidance, action smoothness, collision penalty, time penalty, and progress), together with a LiDAR-based safety gate that prevents unsafe motions. The system is implemented in ROS + Gazebo (TurtleBot3) and evaluated with PathBench metrics, including success rate, collision rate, path efficiency, and re-planning efficiency, in dynamic and partially observable environments. Experiments show improved success rate and sample efficiency over single-algorithm baselines (DQN or TD3 alone) and rule-based planners, with better generalization to unseen obstacle configurations and reduced abrupt control changes. Code and evaluation scripts are available at the project repository.

💡 Deep Analysis

📄 Full Content

Reference