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Updated: Jun 22, 2025
11:53
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
Published on: October 14, 2017
11.6K
GeneWorker: An end-to-end robotic reinforcement learning approach with collaborative generator and worker networks
Hao Wang1, Hengyu Man2, Wenxue Cui3
1Faculty of Computing, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China; College of Engineering, City University of Hong Kong, Kowloon, 999077, Hong Kong Special Administrative Region of China.
Summary
This study introduces a novel neural skill representation for reinforcement learning, enabling robots to learn complex behaviors more effectively. The GeneWorker algorithm achieves over 90% success in robotic tasks, significantly improving upon existing methods.
Area of Science:
- Robotics
- Artificial Intelligence
- Machine Learning
Background:
- Current reinforcement learning (RL) skills are limited abstractions of action sequences.
- Fixed network parameters in RL hinder integration with meta-learning and large language models.
- A more flexible skill representation is needed for advanced autonomous agents.
Purpose of the Study:
- To propose a novel neural skill representation based on neuron activation across layers.
- To introduce an end-to-end robotic reinforcement learning algorithm, GeneWorker, utilizing this representation.
- To enable adaptable network parameters for improved decision-making in diverse environmental conditions.
Main Methods:
- Developed a unique neural skill representation abstracting neuron activations.
- Designed a GeneWorker algorithm with collaborative generator and worker sub-networks.
- Generator produces multi-spatial neural skills; worker integrates skills to adapt network parameters.
Main Results:
- GeneWorker achieved a mean success rate exceeding 90.67% on continuous robotic tasks.
- The algorithm demonstrated superior performance compared to state-of-the-art methods.
- Achieved a minimum improvement of 54% on the pick-and-place task.
Conclusions:
- The proposed neural skill representation enhances RL capabilities for autonomous agents.
- GeneWorker offers a flexible and effective approach to robotic reinforcement learning.
- This method facilitates adaptable network parameters for robust performance in dynamic environments.
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