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Related Experiment Video

Updated: Dec 15, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Learning Reward Function with Matching Network for Mapless Navigation.

Qichen Zhang^1,2, Meiqiang Zhu^1,2, Liang Zou^1,2

¹Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, Xuzhou 221116 China.

Sensors (Basel, Switzerland)

|July 8, 2020

Summary

This study introduces a novel reward function for deep reinforcement learning (DRL) in mapless navigation. The matching network-based reward function accelerates training and improves performance without human supervision.

Keywords:

deep reinforcement learning matching network navigation reward shaping

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Area of Science:

Robotics
Artificial Intelligence
Machine Learning

Background:

Deep reinforcement learning (DRL) is effective for mapless navigation.
Designing effective reward functions for DRL agents is challenging and relies heavily on designer expertise.
Existing methods often require extensive tuning and lack adaptability to new tasks.

Purpose of the Study:

To develop a general and robust reward function for DRL-based mapless navigation.
To enable reward shaping from existing trajectories without human supervision.
To accelerate DRL training and improve navigation performance in diverse environments.

Main Methods:

Proposed a novel reward function utilizing a matching network (MN).
Employed reward shaping from trajectories of similar navigation tasks for pre-training.
The MN-based reward function learns from experience across different tasks to inform new ones.
Ensured the proposed reward function preserves the optimal strategy of the DRL agent.

Main Results:

DRL agents converged with fewer iterations using the learned reward function compared to state-of-the-art methods on static maps.
The method demonstrated robust performance in dynamic environments with partially moving obstacles.
The strategy successfully completed navigation tasks on unseen test maps without additional training.

Conclusions:

The proposed MN-based reward function effectively addresses the challenge of reward function design in DRL for mapless navigation.
This approach enhances training efficiency and generalization capabilities of DRL agents.
The method offers a promising solution for autonomous navigation in complex and dynamic environments.