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Policy Compression for Intelligent Continuous Control on Low-Power Edge Devices.

Thomas Avé¹, Tom De Schepper², Kevin Mets³

¹IDLab-Department of Computer Science, University of Antwerp-IMEC, Sint-Pietersvliet 7, 2000 Antwerp, Belgium.

Sensors (Basel, Switzerland)

|August 10, 2024

Summary

Researchers developed a new policy distillation method to compress deep reinforcement learning (DRL) models for continuous control tasks on edge devices. This technique effectively reduces model size while maintaining or improving performance for robotics and IoT applications.

Keywords:

DRL continuous action spaces edge computing model compression policy distillation soft actor-critic

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Area of Science:

Artificial Intelligence
Robotics
Computer Science

Background:

Deep reinforcement learning (DRL) offers real-time inference on edge devices, enhancing privacy and reliability for applications like Autonomous Mobile Robots (AMRs) and Internet of Things (IoT) devices.
Deploying energy-intensive DRL models on power-constrained edge devices is challenging, necessitating model compression techniques.
Policy distillation is a popular compression method, but existing approaches do not support continuous action spaces common in robotics.

Purpose of the Study:

To adapt policy distillation for compressing DRL models used in continuous control tasks.
To maintain the stochastic nature of continuous DRL algorithms during compression.
To enable efficient deployment of DRL on power-constrained edge devices for real-world applications.

Main Methods:

Developed an improved policy distillation method specifically for continuous action spaces.
Focused on preserving the inherent stochasticity of continuous DRL algorithms.
Applied the method to compress DRL policies for continuous control tasks.

Main Results:

Successfully compressed DRL policies for continuous control tasks by up to 750%.
Achieved performance levels that maintained or exceeded the original teacher model's capabilities by up to 41%.
Demonstrated effectiveness on two popular continuous control benchmarks.

Conclusions:

The enhanced policy distillation method effectively compresses DRL models for continuous control tasks.
This advancement facilitates the deployment of sophisticated DRL on resource-limited edge devices.
The technique shows promise for improving the efficiency and performance of AI in robotics and IoT.