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Related Experiment Video

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Automated Visual Cognitive Tasks for Recording Neural Activity Using a Floor Projection Maze

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A reinforcement learning algorithm acquires demonstration from the training agent by dividing the task space.

Lipeng Zu¹, Xiao He¹, Jia Yang¹

¹State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; University of Chinese Academy of Sciences, Beijing, 100049, China.

Neural Networks : the Official Journal of the International Neural Network Society

|May 15, 2023

Summary

This summary is machine-generated.

This study introduces a novel self-imitation learning algorithm for reinforcement learning (RL) agents. It enables efficient learning from demonstrations in reward-sparse environments, improving robot control success rates.

Keywords:

Reinforcement learning Robotic grasping Self-imitation learning Sparse reward function Task space division

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Last Updated: Jul 30, 2025

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Area of Science:

Robotics
Artificial Intelligence
Machine Learning

Background:

Reinforcement learning (RL) faces challenges in reward-sparse environments.
Existing methods often rely on expert demonstrations, which may be suboptimal.
Learning from imperfect demonstrations is a significant hurdle in real-world applications.

Purpose of the Study:

To propose a self-imitation learning algorithm for efficient, high-quality demonstration acquisition during training.
To address the limitations of learning from suboptimal expert demonstrations in RL.
To enhance agent performance in reward-sparse settings.

Main Methods:

A self-imitation learning algorithm is proposed, utilizing task space division.
Well-defined criteria in the task space are used to determine trajectory quality.
The algorithm facilitates learning from self-generated, high-quality demonstrations.

Main Results:

The proposed algorithm improves the success rate of robot control.
A high mean Q value per step is achieved, indicating enhanced learning.
The method demonstrates effectiveness in learning from self-policy demonstrations.

Conclusions:

The developed algorithm shows great potential for learning in sparse environments.
It offers a viable solution for reward-sparse scenarios where task spaces can be divided.
This approach enhances robot control capabilities by leveraging self-imitation learning.