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Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

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Hierarchical curiosity loops and active sensing.

Goren Gordon¹, Ehud Ahissar

¹Department of Neurobiology, Weizmann Institute of Science, Rehovot, 76100, Israel. goren@gorengordon.com

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

|March 6, 2012

Summary

This summary is machine-generated.

This study introduces hierarchical curiosity loops for autonomous agents to learn about their environment without supervision. This model optimizes learning by rewarding prediction errors, enhancing knowledge and skills through a hierarchical approach.

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Area of Science:

Artificial Intelligence
Computational Neuroscience
Robotics

Background:

Autonomous agents require effective learning strategies for environmental interaction.
Current models often lack hierarchical structures for complex skill acquisition.
Unsupervised learning is crucial for agents operating without external guidance.

Purpose of the Study:

To present a novel model of hierarchical curiosity loops for autonomous active learning.
To enable agents to optimize learning of sensory-motor correlations without external supervision.
To demonstrate the model's application in active sensing using a biological system.

Main Methods:

Developed a hierarchical curiosity loop architecture.
Utilized an actor-critic reinforcement learning (RL) paradigm.
Rewarded prediction errors to drive learning.
Modeled the rodent vibrissae (whiskers) system for active sensing.

Main Results:

The model successfully learns sensory-motor correlations.
Hierarchical structure enhances the extent and diversity of learned knowledge and skills.
Demonstrated application in active sensing tasks like free-air whisking and object localization.

Conclusions:

Hierarchical curiosity loops provide an effective framework for autonomous active learning.
The model's architecture supports progressive skill acquisition and knowledge expansion.
This approach has significant implications for developing more capable and adaptive artificial agents.