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Goal-guided greedy experience replay-enhanced reinforcement learning for efficient autonomous navigation
1College of Big Data and Information Engineering, Guizhou University, Guiyang, China.
Scientific Reports
|May 19, 2026
Summary
This study introduces a Goal-guided Greedy Experience Replay Enhanced Reinforcement Learning (GER-RL) method to improve autonomous navigation. By prioritizing valuable experiences, GER-RL enhances data utilization and significantly boosts navigation performance.
Area of Science:
- Robotics
- Artificial Intelligence
- Machine Learning
Background:
- Deep reinforcement learning (DRL) shows promise in mapless goal-driven navigation.
- Current DRL methods suffer from inefficient experience utilization due to uniform sampling.
- This underutilization hinders the learning process and overall navigation performance.
Purpose of the Study:
- To propose an enhanced reinforcement learning method for efficient autonomous navigation.
- To address the issue of insufficient experience utilization in DRL-based mapless navigation.
- To improve data efficiency and navigation performance in autonomous agents.
Main Methods:
- Introduced Goal-guided Greedy Experience Replay Enhanced Reinforcement Learning (GER-RL).
- Implemented non-uniform experience sampling based on experience importance.
- Integrated the prioritized experience sampling into a DRL-based navigation model.
Main Results:
- The GER-RL method effectively prioritizes more beneficial experiences for agent learning.
- Demonstrated significant improvements in data utilization efficiency during the DRL learning process.
- Achieved enhanced navigation performance metrics, including higher success rates and reduced collision rates.
Conclusions:
- The proposed GER-RL method offers a more efficient approach to autonomous navigation.
- Prioritizing experiences significantly enhances the learning efficiency of DRL agents.
- GER-RL provides a substantial advancement over existing DRL methods for mapless goal-driven navigation.
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