Enhancing Image Classification with Temporally Aware Soft Actor-Critic Algorithms for Real-Time Applications

Master thesis (2024)

Authors

P. Năvală Mechanical Engineering

Contributors

M.H.G. Verhaegen Team Michel Verhaegen - Mechanical, Maritime and Materials Engineering (mentor)
O.A. Soloviev Team Michel Verhaegen - Mechanical, Maritime and Materials Engineering (mentor)
A. Dekhovich Team Michel Verhaegen - Mechanical, Maritime and Materials Engineering (mentor)
N.J. Myers Team Nitin Myers - Mechanical, Maritime and Materials Engineering (graduation committee member)
Faculty
Mechanical Engineering
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Published Date

29-08-2024

Language

English

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Faculty

Mechanical Engineering

Abstract

The thesis presents a novel approach to optimizing input computation for minimizing classification error in image classification tasks. It leverages the capabilities of the Soft Actor-Critic (SAC) algorithm, a reinforcement learning method tailored for continuous action spaces. The focus is on developing a real-time adaptable feedback loop that continuously learns and adjusts inputs based on classifier output probabilities. Key to this approach is the incorporation of a Gated Recurrent Unit (GRU) architecture within the SAC framework to capture temporal dependencies, addressing the challenge of ever-increasing state dimensions.

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