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Deep Neural Networks for Image-Based Dietary Assessment

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Deep deterministic policy gradient algorithm: A systematic review.

Ebrahim Hamid Sumiea^1,2, Said Jadid Abdulkadir^1,2, Hitham Seddig Alhussian^1,2

¹Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, Malaysia.

|May 20, 2024

Summary

This review examines Deep Deterministic Policy Gradient (DDPG), a key Deep Reinforcement Learning (DRL) algorithm. It covers DDPG

Keywords:

Deep deterministic policy gradient (DDPG)Deep reinforcement learning (DRL)Hyperparameter Optimization

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Area of Science:

Artificial Intelligence
Machine Learning
Deep Reinforcement Learning

Background:

Deep Reinforcement Learning (DRL) excels at complex decision-making in high-dimensional spaces.
Deep Deterministic Policy Gradient (DDPG) is a prominent DRL algorithm combining value-based and policy-based methods.

Purpose of the Study:

To conduct a comprehensive review of recent advancements, trends, challenges, and opportunities in DDPG.
To provide researchers with valuable insights into DDPG methods and techniques within the DRL field.

Main Methods:

Systematic literature search across Scopus, Web of Science, and ScienceDirect.
Analysis of 85 relevant studies published between 2018 and 2023.
Overview of DDPG formulation, implementation, training, and applications.

Main Results:

Detailed examination of DDPG's core concepts and components.
Highlighting diverse applications including Autonomous Driving, UAVs, Resource Allocation, IoT, Robotics, and Finance.
Comparative analysis of DDPG against other DRL and traditional RL methods, detailing strengths and weaknesses.

Conclusions:

DDPG is a versatile algorithm with broad applicability across various domains.
The review serves as a crucial resource for understanding and advancing DDPG research and application.